Systems and methods for reconstruction of a vehicular crash

ABSTRACT

A system for reconstructing a vehicular crash (i) receives sensor data of a vehicular crash from at least one mobile device associated with a user; (ii) generates a scenario model of the vehicular crash based upon the received sensor data; (iii) transmits the scenario model to a user computer device associated with the user; (iv) receives a confirmation of the scenario model from the user computer device; (v) stores the scenario model; and (vi) may generate at least one insurance claim form based upon the scenario model. As a result, the speed and accuracy of the claim processing is increased. The system may also utilize vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle features, and/or generate virtual reconstructions of the vehicle collision.

RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 15/415,315, filed Jan. 25, 2017, and entitled“SYSTEMS AND METHODS FOR RECONSTRUCTION OF A VEHICULAR CRASH,” whichclaims priority to U.S. Provisional Patent Application No. 62/328,422,filed Apr. 27, 2016, entitled “SYSTEMS AND METHODS FOR RECONSTRUCTION OFA VEHICULAR CRASH,” U.S. Provisional Patent Application No. 62/332,350,filed May 5, 2016, entitled “SYSTEMS AND METHODS FOR RECONSTRUCTION OF AVEHICULAR CRASH,” U.S. Provisional Patent Application No. 62/359,842,filed Jul. 8, 2016, entitled “SYSTEMS AND METHODS FOR RECONSTRUCTION OFA VEHICULAR CRASH,” U.S. Provisional Patent Application No. 62/402,445,filed Sep. 30, 2016, entitled “SYSTEMS AND METHODS FOR RECONSTRUCTION OFA VEHICULAR CRASH,” and U.S. Provisional Patent Application No.62/413,610, filed Oct. 27, 2016, entitled “SYSTEMS AND METHODS FORRECONSTRUCTION OF A VEHICULAR CRASH,” the entire contents anddisclosures of which are hereby incorporated by reference herein intheir entirety.

FIELD OF THE INVENTION

The present disclosure relates to reconstruction of a vehicular crashand, more particularly, to a network-based system and method forreconstructing a vehicular crash or other collision based upon sensordata and determining a severity of the vehicular crash based upon thereconstruction.

BACKGROUND

Due to shock or other factors human perception may be unreliable in atraumatic situation like an accident, especially a vehicular crash. Insome cases, some accident victims aren't even aware that they may havesustained injury and cause greater harm to themselves by delayingmedical evaluation until their condition has worsened. Furthermore, thehuman reports of an accident may be inaccurate due to mistake, a desireto shift blame, or in some cases, a desire to overstate the severity ofthe accident for fraud purposes. Typed or illustrated accidentdescriptions, usually are created after the fact. In some cases, thesereports are created days after the accident. These accident descriptionsmay include a plurality of errors due to issues with human perceptionand/or memory. The accident descriptions may be created based upon inputfrom the parties involved in the accident, witnesses to the accident,and first responders at the scene of the accident. It is desired to havea system that accurately records aspects of an accident so that humanmemory and perception is not the only basis for reconstructing andreporting an accident.

BRIEF SUMMARY

The present embodiments may relate to systems and methods forreconstructing a vehicular crash. An accident monitoring system, asdescribed herein, may include an accident monitoring (“AM”) computerdevice that is in communication with a mobile computer device associatedwith a user. The AM computer device may be configured to (i) receivesensor data of a vehicular crash from at least one mobile deviceassociated with a user, (a) wherein the at least one mobile deviceincludes at least one of a vehicle computer device associated with thevehicle involved in the vehicular crash, a cellular connected computerdevice (e.g., a cellphone, a tablet, a wearable device, etc.), and anInternet connected computer device (e.g., a smartphone, a tablet, awearable device, etc.), (b) wherein the at least one mobile deviceincludes one or more sensors including sensors that may be embeddedwithin the at least one mobile device and/or included within the vehiclefor measuring or recording parameters experienced by the vehicle orwithin the vehicle, and (c) wherein the sensor data is based upon aperiod of time prior to the vehicular crash and continuing through to aperiod of time after the vehicular crash and includes at least one of ameasurement of at least one of speed, direction rate of acceleration,rate of deceleration, location, position, orientation, and rotation ofthe vehicle, a measurement of one or more changes to at least one ofspeed, direction rate of acceleration, rate of deceleration, location,position, orientation, and rotation of the vehicle, a number ofoccupants in the vehicle, a location of occupants, a position ofoccupants (i.e., the location and orientation of an occupant's bodyparts relative to the vehicle), seatbelt sensor data, and seat occupantweight sensor data; (ii) store a database of vehicular crash scenariosbased upon past vehicular crashes and sensor data associated with thevehicular crash scenarios; (iii) compare the database of vehicular crashscenarios to the received sensor data; (iv) generate a plurality ofscenario models of the vehicular crash based upon the sensor data andthe database of vehicular crash scenarios; (v) determine a certainty ofeach of the plurality of scenario models; (vi) generate the scenariomodel from the plurality of scenario models based upon the certaintyassociated with the scenario model; (vii) select one or more emergencyservices based upon the scenario model and a location of the vehicularcrash; (viii) transmit a message to the one or more emergency servicesbased upon the scenario model, wherein the one or more emergencyservices include at least one of a towing service, an emergency medicalservice provider, a fire department, a police department, and/or someother emergency responder; (ix) transmit the scenario model to a usercomputer device associated with the user, where the user may be one ofan policyholder, one or more occupants of the vehicle, and/or theemergency service personnel; (x) receive one or more changes to thescenario model from the user computer device; (xi) update the scenariomodel based upon the one or more changes; (xii) receive a confirmationof the scenario model from the user computer device; (xiii) determine atleast one potential injury to an occupant of the vehicle based upon thescenario model, wherein the at least one potential injury may be atleast partially determined based upon the location and/or position ofthe occupant within the vehicle; (xiv) transmit the at least onepotential injury to the user computer device; (xv) receive confirmationof the at least one potential injury from the user computer device;(xvi) receive sensor data from a different vehicle involved in thevehicular crash; (xvii) update the scenario model based upon the sensordata from the different vehicle; (xviii) store the scenario model; (xix)update the database of vehicular crash scenarios based upon the storedscenario model; and/or (xx) generate at least one insurance claim formbased upon the scenario model. The AM computing device may includeadditional, less, or alternate functionality, including that discussedelsewhere herein.

At least one advantage of this system is that because the scenario modelis based upon actual sensor data during the vehicular crash, theaccuracy of the scenario model is greatly increased. This reduces thereliance on potentially faulty human recollection. Furthermore, the useof sensor data allows for quicker generation of the scenario modeldecreasing the lead time for claim processing. Another advantage of thesystem is that by providing accurate information about the vehicularcrash to emergency personnel, the proper personnel and services may beefficiently routed to the vehicular crash location. This may reduce thechance that an injury may be overlooked, and reduce the time that avehicular occupant has to wait for emergency services. A furtheradvantage is that the system may be able to predict injuries caused bythe accident. Another advantage of the system is reducing potentialinjuries in a vehicular accident by inducing passengers to repositionand/or change direction of facing. A further advantage is reducingdamage to at least one of a vehicle and/or passengers by repositioningthe passengers prior to impact.

In one aspect, a computer system for reconstructing a vehicular crashmay be provided. The computer system may include at least one processor(and/or associated transceiver) in communication with at least onememory device. The at least one processor (and/or associatedtransceiver) may be configured or programmed to: (1) receive sensor dataof a vehicular crash from at least one mobile device associated with auser (such as via wireless communication or data transmission over oneor more radio links or wireless communication channels); (2) generate ascenario model of the vehicular crash based upon the received sensordata; (3) transmit the scenario model to a user computer deviceassociated with the user (such as via wireless communication or datatransmission over one or more radio links or wireless communicationchannels); (4) receive a confirmation of the scenario model from theuser computer device (such as via wireless communication or datatransmission over one or more radio links or wireless communicationchannels); (5) store the scenario model; and/or (6) generate at leastone insurance claim form based upon the scenario model to facilitatequickly and accurately processing an insurance claim. The computersystem may have additional, less, or alternate functionality, includingthat discussed elsewhere herein.

In another aspect, a computer-based method for reconstructing avehicular crash may be provided. The method may be implemented on anaccident monitoring (“AM”) server that includes at least one processor(and/or associated transceiver) in communication with at least onememory device. The method may include: (1) receiving, at the AM server,sensor data of a vehicular crash from at least one mobile deviceassociated with a user (such as via wireless communication or datatransmission over one or more radio links or wireless communicationchannels); (2) generating, by the AM server, a scenario model of thevehicular crash based upon the received sensor data; (3) transmittingthe scenario model to a user computer device associated with the user(such as via wireless communication or data transmission over one ormore radio links or wireless communication channels); (4) receiving aconfirmation of the scenario model from the user computer device (suchas via wireless communication or data transmission over one or moreradio links or wireless communication channels); (5) storing, in thememory, the scenario model; and/or (6) generating, by the AM server, atleast one insurance claim form based upon the scenario model tofacilitate quickly and accurately processing an insurance claim. Thecomputer system may have additional, less, or alternate functionality,including that discussed elsewhere herein.

In yet another aspect, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonmay be provided. When executed by at least one processor, thecomputer-executable instructions cause the processor (and/or anassociated transceiver) to: (1) receive sensor data of a vehicular crashfrom at least one mobile device associated with a user (such as viawireless communication or data transmission over one or more radio linksor wireless communication channels); (2) generate a scenario model ofthe vehicular crash based upon the received sensor data; (3) transmitthe scenario model to a user computer device associated with the user(such as via wireless communication or data transmission over one ormore radio links or wireless communication channels); (4) receive aconfirmation of the scenario model from the user computer device (suchas via wireless communication or data transmission over one or moreradio links or wireless communication channels); (5) store the scenariomodel; and/or (6) generate at least one insurance claim form based uponthe scenario model to facilitate quickly and accurately processing aninsurance claim. The computer-executable instructions may directadditional, less, or alternate functionality, including that discussedelsewhere herein.

In one aspect, a computer system for notifying emergency services of avehicular crash may be provided. The computer system may include atleast one processor in communication with at least one memory device.The at least one processor may be configured or programmed to: (1)receive sensor data of a vehicular crash from at least one mobile deviceassociated with a user; (2) generate a scenario model of the vehicularcrash based upon the received sensor data; (3) store the scenario model;and/or (4) transmit a message to one or more emergency services basedupon the scenario model to facilitate quickly and accurately deployingemergency services to the vehicular crash location. The computer systemmay have additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

In another aspect, a computer-based method for notifying emergencyservices of a vehicular crash may be provided. The method may beimplemented on an accident monitoring (“AM”) server that includes atleast one processor in communication with at least one memory device.The method may include: (1) receiving, at the AM server, sensor data ofa vehicular crash from at least one mobile device associated with auser; (2) generating, by the AM server, a scenario model of thevehicular crash based upon the received sensor data; (3) storing, in thememory, the scenario model; and/or (4) transmitting a message to one ormore emergency services based upon the scenario model to facilitatequickly and accurately deploying emergency services to the vehicularcrash location. The computer system may have additional, less, oralternate functionality, including that discussed elsewhere herein.

In yet another aspect, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonmay be provided. When executed by at least one processor, thecomputer-executable instructions cause the processor to: (1) receivesensor data of a vehicular crash from at least one mobile deviceassociated with a user; (2) generate a scenario model of the vehicularcrash based upon the received sensor data; (3) store the scenario model;and/or (4) transmit a message to one or more emergency services basedupon the scenario model to facilitate quickly and accurately deployingemergency services to the vehicular crash location. Thecomputer-executable instructions may direct additional, less, oralternate functionality, including that discussed elsewhere herein.

In still another aspect, a computer system for detecting a vehicularcrash may be provided. The computer system may include at least oneprocessor, sensor, and/or transceiver in communication with at least onememory device, the at least one processor, sensor, and/or transceiver.The at least one processor may be programmed to (1) receive data fromsaid at least one sensor; (2) determine that a potential vehicular crashis imminent based upon the received data; and/or (3) transmit one ormore high priority packets including a notification that the potentialvehicular crash is imminent. The computer system may include additional,less, or alternate functionality, including that discussed elsewhereherein.

In a different aspect, a computer-based method for detecting a vehicularcrash may be provided. The method may include (1) receiving data from asensor; (2) determining that a potential vehicular crash is imminentbased upon the received data; and/or (3) transmitting one or more highpriority packets including a notification that the potential vehicularcrash is imminent. The method may include additional, less, or alternateactions, including those discussed elsewhere herein.

In still another aspect, a computer system for detecting a vehicularcrash may be provided. The computer system may include at least oneprocessor, sensor, and/or transceiver in communication with at least onememory device. The at least one processor may be programmed to (1)(locally or remotely) receive occupant data from at least one internalsensor (such as via wired or wireless communication); (2) (locally orremotely) receive external data from at least one external sensor (suchas via wired or wireless communication); (3) determine that a potentialvehicular crash is imminent based upon the received external data; (4)determine positional information for at least one occupant of a vehicleand/or (5) perform at least one action to reduce a severity of apotential injury to the at least one occupant prior to impact. Thecomputer system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

In a different aspect, a computer-based method for detecting a vehicularcrash may be provided. The method may include (1) (locally or remotely)receiving occupant data from at least one internal sensor (such as viawired or wireless communication); (2) (locally or remotely) receivingexternal data from at least one external sensor (such as via wired orwireless communication); (3) determining that a potential vehicularcrash is imminent based upon the received external data; (4) determiningpositional information for at least one occupant of a vehicle and/or (5)performing at least one action to reduce a severity of a potentialinjury to the at least one occupant prior to impact. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

In still another aspect, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonmay be provided. When executed by at least one processor, thecomputer-executable instructions cause the processor (and/or anassociated transceiver) to: (1) (locally or remotely) receive occupantdata from at least one internal sensor (such as via wired or wirelesscommunication); (2) (locally or remotely) receive external data from atleast one external sensor (such as via wired or wireless communication);(3) determine that a potential vehicular crash is imminent based uponthe received external data; (4) determine positional information for atleast one occupant of a vehicle and/or (5) perform at least one actionto reduce a severity of a potential injury to the at least one occupantprior to impact. The storage media may include additional, less, oralternate actions, including those discussed elsewhere herein.

In still another aspect, a computer-based method for detecting avehicular collision and automatically engaging an autonomous vehiclefeature is provided. The method is implemented on a vehicle computerdevice that includes one or more processors, sensors, and/ortransceivers in communication with at least one memory device. Themethod includes, via one or more processors, sensors, and/ortransceivers, (1) receiving occupant data from at least one internalsensor, (2) receiving external data from the at least one externalsensor, (3) determining by the vehicle computer device that a potentialvehicular collision is imminent based upon the received external data,and/or (4) automatically engaging at least one of steering, braking, andacceleration of the vehicle to prevent or mitigate damage caused by thevehicle collision. The method may include additional, less, or alternateautonomous-related actions, including those discussed elsewhere herein.

Advantages will become more apparent to those skilled in the art fromthe following description of the preferred embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of the systems andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed systemsand methods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and are instrumentalitiesshown, wherein:

FIG. 1 illustrates a schematic diagram of an exemplary vehicle.

FIG. 2 illustrates a flow chart of an exemplary process ofreconstructing a vehicular crash, such as of the vehicle shown in FIG.1.

FIG. 3 illustrates a flow chart of an exemplary computer-implementedprocess for reconstructing a vehicular crash shown in FIG. 2.

FIG. 4 illustrates a simplified block diagram of an exemplary computersystem for implementing the process shown in FIG. 1.

FIG. 5 illustrates an exemplary configuration of a client computerdevice shown in FIG. 4, in accordance with one embodiment of the presentdisclosure.

FIG. 6 illustrates an exemplary configuration of a server shown in FIG.4, in accordance with one embodiment of the present disclosure.

FIG. 7 illustrates a flow chart of an exemplary computer-implementedprocess of notifying emergency services of a vehicular crash using thesystem shown in FIG. 4.

FIG. 8 illustrates a flow chart of an exemplary computer-implementedprocess of detecting a vehicular crash using the system shown in FIG. 4.

FIG. 9 illustrates a flow chart of another exemplarycomputer-implemented process of detecting (and/or reconstructing) avehicular crash using the system shown in FIG. 4.

FIG. 10 illustrates a diagram of components of one or more exemplarycomputing devices that may be used in the system shown in FIG. 4.

FIG. 11 illustrates a flow chart of an exemplary computer-implementedprocess of estimating an extent of injury to vehicle occupants resultingfrom a vehicle collision using the system shown in FIG. 4.

The Figures depict preferred embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION OF THE DRAWINGS

The present embodiments may relate to, inter alia, systems and methodsfor reconstructing a vehicular crash or other accident based upon sensordata and determining a severity of the vehicular crash based upon thereconstruction. In an exemplary embodiment, the process is performed byan accident monitoring (“AM”) computer device, also known as an accidentmonitoring (“AM”) server.

Furthermore, first responders and other emergency personnel may not beaware of the severity of injuries that may have occurred as a result ofthe accident until they are at the scene. As every minute may count inan emergency, the more information they receive the better that thefirst responders may prepare for and treat potential injuries.

In one embodiment, the user may have installed an application on amobile device, such as a smart phone, that allows the mobile device tomonitor for accidents and transmit sensor data to the AM server. Inanother embodiment, the user's vehicle includes a vehicle computerdevice in communication with one or more sensors for detecting vehicleparameters and/or conditions, also known as sensor data. The vehiclecomputer device is configured to monitor the sensor data for accidentsand transmit the sensor data to the AM server.

In the exemplary embodiment, the AM server may receive sensor data of avehicular crash from at least one mobile computer device associated withthe user (e.g., a cellphone, a tablet, a wearable electronic, or avehicle computer device). In the example embodiment, the user is aninsurance policyholder associated with at least one vehicle that isinvolved in a vehicular crash or accident. The user may be the driver ora passenger of the vehicle.

In the exemplary embodiment, the mobile computer device receives thesensor data from at least one sensor associated with the mobile computerdevice. In some embodiments, at least one sensor may be one or more of aplurality of sensors in the vehicle. In other embodiments, the at leastone sensor may be an accelerometer, gyroscope, camera, compass, and/oranother sensor in the user's smartphone or other mobile device. The atleast one sensor may include cabin-facing sensors that may be configuredto collect sensor data associated with occupants (i.e., a driver and apassenger) and/or luggage within the vehicle, such as a location and/ora position of the occupants. The location of an occupant may refer to aparticular seat or other portion of the vehicle where the occupant islocated. The position of an occupant may refer to a body and/or limborientation of an occupant relative to the vehicle or components of thevehicle, such as a steering wheel, a front portion of the vehicle, andthe like. In some embodiments, the sensors may be configured to collectsensor data associated with weight distribution information of thevehicle, occupants, luggage, fuel, and so forth.

In the exemplary embodiment, the sensor data provides information aboutthe vehicular crash. This information includes, but is not limited to,vehicular conditions prior to, during, and after the accident or crash(i.e., speed, acceleration, location, direction of travel, informationabout surroundings, and operating conditions), forces and directions offorces experienced and/or received during the accident (i.e., changes inspeed and direction), information about the occupants of the vehicle(i.e., number, weight, location, position, and seatbelt status), detailsabout the vehicle (i.e., make, model, and mileage), and any actions thatthe vehicle took during the accident (i.e., airbag deployment). In someembodiments, sensor data may include data for a period of time prior tothe vehicular accident and continue through a period of time after thevehicular accident.

In some embodiments, a vehicle controller in the vehicle collects thesensor data from sensors and transmits the sensor data to the AM server.In other embodiments, the user's mobile device transmits its collectedsensor data to the AM server. In still other embodiments, the user'smobile device is in communication with the vehicle controller. In theseother embodiments, the user's mobile device transmits its collectedsensor data to the vehicle controller and the vehicle controllertransmits the sensor data from the user's mobile device and from thevehicle's sensors to the AM server.

In the exemplary embodiment, the AM server may generate a scenario modelof the vehicular crash based upon the received sensor data. Scenariomodels may predict damage to the vehicle and injuries that may beexperiences by the driver and passengers of the vehicle. In theexemplary embodiment, that AM server may access a database that maycontain a plurality of crash scenarios and the sensor data associatedwith these crash scenarios. The scenarios may be based upon informationfrom vehicle crash testing facilities, from past crashes that that AMserver has analyzed, and/or from other sources that allow that AM serverto operate as described herein. The AM server may compare the receivedsensor data with the different stored crash scenarios to generate ascenario model that is the most likely match for the vehicular crash.For example, that AM server may determine that that vehicle wasrear-ended by another vehicle that was going approximately 30 miles anhour while that vehicle was stopped.

In some embodiments, that AM server generates a plurality of scenariomodels that may fit the sensor data received. The AM server may thenrank the generated scenarios based upon the likelihood or degree ofcertainty that the scenario is correct. In some further embodiments,that AM server may compare the degree of certainty to a predeterminedthreshold.

The AM server may transmit the scenario model to a user computer deviceassociated with user. In some embodiments, the user computer device isassociated with the policyholder of an account associated with thevehicle. In other embodiments, the user computer device is associatedwith one of the occupants of the vehicle. In still other embodiments,the user computer device is associated with a third party, such as oneof the emergency service personnel.

The user computer device may then display the scenario model to theuser. The user computer device may be configured to receive input fromthat user about the displayed scenario model. In some embodiments, thatuser computer device may receive a confirmation from that user that thescenario model is correct. The user computer device may then transmitthe confirmation for the scenario model to that AM server.

The AM server may store the scenario model. Based upon the storedscenario model for the vehicular crash, the AM server may generate atleast one insurance claim form for the vehicular accident. In theexemplary embodiment, the AM server retrieves one or more stored blankclaim forms, such as from a database. The AM server may then populateone or more of the blank fields of the retrieved blank forms based uponthe scenario model. Based upon the completeness of the stored scenariomodel, the AM server may be able to fill out multiple forms and describethe accident in detail. The AM server may also be able to determine oneor more damages that the vehicle would have incurred in the vehicularaccident. The AM server may also be able to estimate a cost of repairsor replacement.

In some embodiments, the AM server may add the stored scenario model andthe associated sensor data to the database to improve the stored models.In these embodiments, the AM server may be executing one or more machinelearning algorithms to improve the accuracy of the generating scenariomodels.

In other embodiments, the user computer device may receive changesand/or updates to the scenario model from the user. The AM server mayupdate the scenario model based upon the updates received from the user.

In some embodiments, the AM server may be able to determine one or morepotential injuries to one or more occupants of the vehicle based uponthe scenario model. For example, in the rear-end accident example, theAM server may determine that there is a 40% chance that the driver mayhave incurred a minor neck injury. The AM server may be configured toidentify a position of each occupant from the scenario model anddetermine any potential injuries that may be associated with theidentified position. In one example, the AM server determines a frontseat passenger is turned around during the crash to protect a child inthe back seat of the vehicle. Accordingly, the front seat passenger mayhave suffered a spinal injury based upon his or her position. The AMserver may then transmit the one or more potential injuries to userdevice for confirmation by the user. Based upon which injuries that theuser indicates where incurred, the AM server may then update thescenario model.

In some further embodiments, the AM server may receive sensor data frommore than one vehicle involved in the vehicular accident. The AM servermay then combine the sensor data from the multiple vehicles to updatethe scenario model. The AM server may also expand the scenario model forthe occupants of each other vehicle involved in the vehicular accident.

In some further embodiments, the AM server may be in communication withone or more emergency services providers, such as a towing service, anemergency medical service provider, a police department, a firedepartment, and/or some other emergency responder. The AM server maycontact one or more of these emergency service providers based upon thescenario model and the location of the vehicular crash. For example, inthe rear-end example, the AM server may contact the nearest emergencymedical service provider and request immediate deployment for treatmentof a potential neck injury. The AM server may also provide otherinformation from the scenario model to the contacted emergency serviceprovider.

In some embodiments, the AM server may determine a severity of thevehicular accident. The determined severity may be based upon aplurality of levels of severity, such as set by government or astandards setting organization. Examples may include, but are notlimited to, vehicle damage scale, damage severity code, and injuryseverity score. The AM server may transmit the determined severity tothe one or more emergency services providers. The AM server may also usethe determined severity in generating the claim forms.

At least one of the technical problems addressed by this system mayinclude: (i) improving speed and efficiency of processing a claim basedupon a vehicular accident; (ii) improving the speed and accuracy ofreconstructing a vehicular accident scenario; (iii) properly informingemergency service personnel of the potential injuries associated with avehicular accident; and/or (iv) determining that a vehicular accident isoccurring or may be occurring.

The technical effect achieved by this system may be at least one of: (i)automated reconstruction of a vehicular accident; (ii) automatedpopulation of insurance claim forms; (iii) automated contact ofemergency service personnel; (iv) providing information about thevehicular accident prior to the arrival of the emergency servicepersonnel on the scene; (v) improved speed of emergency service responseto vehicular accidents; and/or (vi) automated detection of vehicularaccidents as they are occurring.

The methods and systems described herein may be implemented usingcomputer programming or engineering techniques including computersoftware, firmware, hardware, or any combination or subset thereof,wherein the technical effects may be achieved by performing at least oneof the following steps: (a) receiving, at an accident monitoring (“AM”)server, sensor data of a vehicular crash from at least one mobile deviceassociated with a user; (b) generating, by the AM server, a scenariomodel of the vehicular crash based upon the received sensor data; (c)transmitting the scenario model to a user computer device associatedwith the user; (d) receiving a confirmation of the scenario model fromthe user computer device; (e) storing, in the memory, the scenariomodel; and/or (f) generating, by the AM server, at least one insuranceclaim form based upon the scenario model to facilitate quickly andaccurately processing an insurance claim to facilitate quickly andaccurately processing an insurance claim.

Additional technical effects may be achieved by performing at least oneof the following steps: (a) receiving, at the AM server, sensor data ofa vehicular crash from at least one mobile device associated with auser; (b) generating, by the AM server, a scenario model of thevehicular crash based upon the received sensor data; (c) storing, in thememory, the scenario model; and/or (d) transmitting a message to one ormore emergency services based upon the scenario model to facilitatequickly and accurately deploying emergency services to the vehicularcrash location.

Further technical effects may be achieved by performing at least one ofthe following steps: (a) receiving data from a sensor; (b) determiningthat a potential vehicular crash is imminent based upon the receiveddata; and/or (c) transmitting one or more high priority packetsincluding a notification that the potential vehicular crash is imminent

Exemplary Vehicle

FIG. 1 depicts a view of an exemplary vehicle 100. In some embodiments,vehicle 100 may be an autonomous or semi-autonomous vehicle capable offulfilling the transportation capabilities of a traditional automobileor other vehicle. In these embodiments, vehicle 100 may be capable ofsensing its environment and navigating without human input. In otherembodiments, vehicle 100 is a manual vehicle, such as a traditionalautomobile that is controlled by a driver 115.

Vehicle 100 may include a plurality of sensors 105 and a vehiclecontroller 110. The plurality of sensors 105 may detect the currentsurroundings and location of vehicle 100. Plurality of sensors 105 mayinclude, but are not limited to, radar, LIDAR, Global Positioning System(GPS), video devices, imaging devices, cameras, audio recorders, andcomputer vision. Plurality of sensors 105 may also include sensors thatdetect conditions of vehicle 100, such as speed, acceleration, gear,braking, and other conditions related to the operation of vehicle 100,for example: at least one of a measurement of at least one of speed,direction rate of acceleration, rate of deceleration, location,position, orientation, and rotation of the vehicle, and a measurement ofone or more changes to at least one of speed, direction rate ofacceleration, rate of deceleration, location, position, orientation, androtation of the vehicle. Furthermore, plurality of sensors 105 mayinclude impact sensors that detect impacts to vehicle 100, includingforce and direction and sensors that detect actions of vehicle 100, suchthe deployment of airbags. In some embodiments, plurality of sensors 105may detect the presence of driver 115 and one or more passengers 120 invehicle 100. In these embodiments, plurality of sensors 105 may detectthe presence of fastened seatbelts, the weight in each seat in vehicle100, heat signatures, or any other method of detecting information aboutdriver 115 and passengers 120 in vehicle 100.

In some embodiments, plurality of sensors 105 may include sensors fordetermining weight distribution information of vehicle 100. Weightdistribution information may include, but is not limited to, the weightand location of remaining gas, luggage, occupants, and/or othercomponents of vehicle 100. In some embodiments, plurality of sensors 105may include sensors for determining remaining gas, luggage weight,occupant body weight, and/or other weight distribution information. Incertain embodiments, plurality of sensors 105 may include occupantposition sensors to determine a location and/or position of eachoccupant (i.e., driver 115 and passengers 120) in vehicle 100. Thelocation of an occupant may identify a particular seat or other locationwithin vehicle 100 where the occupant is located. The position of theoccupant may include the occupant's body orientation, the location ofspecific limbs, and/or other positional information. In one example,plurality of sensors 105 may include an in-cabin facing camera, LIDAR,radar, weight sensors, accelerometer, gyroscope, compass and/or othertypes of sensors to identify the location and/or position of occupantswithin vehicle 100. Vehicle controller 110 and/or another computingdevice(s) (e.g., mobile device(s)) may be configured to monitor sensordata from plurality of sensors 105 and/or other sensors to determineweight distribution information and/or location and position of theoccupants. In one example, vehicle controller 110 may compare sensordata for a particular event (e.g., a road bump) with historical sensordata to identify the weight distribution of vehicle 100 and/or thelocation of the occupants of vehicle 100. In another example, pluralityof sensors 105 may include weight sensors that vehicle controller 110monitors to determine the weight distribution information.

Vehicle controller 110 may interpret the sensory information to identifyappropriate navigation paths, detect threats, and react to conditions.In some embodiments, vehicle controller 110 may be able to communicatewith one or more remote computer devices, such as mobile device 125. Inthe example embodiment, mobile device 125 is associated with driver 115and includes one or more internal sensors, such as an accelerometer, agyroscope, and/or a compass. Mobile device 125 may be capable ofcommunicating with vehicle controller 110 wirelessly. In addition,vehicle controller 110 and mobile device may be configured tocommunicate with computer devices located remotely from vehicle 100.

In some embodiments, vehicle 100 may include autonomous orsemi-autonomous vehicle-related functionality or technology that may beused with the present embodiments to replace human driver actions mayinclude and/or be related to the following types of functionality: (a)fully autonomous (driverless); (b) limited driver control; (c)vehicle-to-vehicle (V2V) wireless communication; (d)vehicle-to-infrastructure (and/or vice versa) wireless communication;(e) automatic or semi-automatic steering; (f) automatic orsemi-automatic acceleration; (g) automatic or semi-automatic braking;(h) automatic or semi-automatic blind spot monitoring; (i) automatic orsemi-automatic collision warning; (j) adaptive cruise control; (k)automatic or semi-automatic parking/parking assistance; (l) automatic orsemi-automatic collision preparation (windows roll up, seat adjustsupright, brakes pre-charge, etc.); (m) driver acuity/alertnessmonitoring; (n) pedestrian detection; (o) autonomous or semi-autonomousbackup systems; (p) road mapping systems; (q) software security andanti-hacking measures; (r) theft prevention/automatic return; (s)automatic or semi-automatic driving without occupants; and/or otherfunctionality. In these embodiments, the autonomous or semi-autonomousvehicle-related functionality or technology may be controlled, operated,and/or in communication with vehicle controller 110.

The wireless communication-based autonomous or semi-autonomous vehicletechnology or functionality may include and/or be related to: automaticor semi-automatic steering; automatic or semi-automatic accelerationand/or braking; automatic or semi-automatic blind spot monitoring;automatic or semi-automatic collision warning; adaptive cruise control;and/or automatic or semi-automatic parking assistance. Additionally oralternatively, the autonomous or semi-autonomous technology orfunctionality may include and/or be related to: driver alertness orresponsive monitoring; pedestrian detection; artificial intelligenceand/or back-up systems; navigation or GPS-related systems; securityand/or anti-hacking measures; and/or theft prevention systems.

While vehicle 100 may be an automobile in the exemplary embodiment, inother embodiments, vehicle 100 may be, but is not limited to, othertypes of ground craft, aircraft, and watercraft vehicles.

Exemplary Process for Reconstructing a Vehicular Crash

FIG. 2 illustrates a flow chart of an exemplary process 200 ofreconstructing a vehicular crash, such as of vehicle 100 shown in FIG.1.

In the exemplary embodiment, a user 202 is associated with a user device204. In the example embodiment, user 202 may be an insurancepolicyholder associated with at least one vehicle 100 that is involvedin a vehicular crash or accident. In some embodiments, user 202 may bedriver 115 or passenger 120 of the at least one vehicle 100. In otherembodiments, user 202 may be an emergency responder and/or some otherwitness to the vehicular crash or accident.

In some embodiments, user device 204 may be mobile device 125 shown inFIG. 1. In these embodiments, user device 204 may be a smart phone,wearable, or other computer device associated with user 202. In otherembodiments, mobile device is vehicle controller 100. In still otherembodiments, user device 204 may represent more than one computer deviceassociated with user 202. In all embodiments, user device 204 may becapable of displaying information to user 202, receiving input from user202, and in communication with an accident monitoring (“AM”) server 206.User device 204 may include software that allows it to function as isdescribed herein.

In the exemplary embodiment, user device 204 detects 208 an accident orvehicular crash. User device 204 transmits 210 data from an accidenttimeframe to AM server 206. In some embodiments, user device 204 maytransmit 210 the data live while the accident is happening. In otherembodiments, user device 204 may transmit 210 the data after theaccident. In the exemplary embodiment, user device 204 may transmit 210stored sensor data from a period before the accident and continues totransmit 210 data for a period of time after the accident.

AM server 206 receives 212 the sensor data. In the exemplary embodiment,AM server 206 may match 214 the data to known vehicular accidentscenarios to determine the most appropriate scenario that matches thereceived sensor data. Based upon the comparison between the sensor dataand each the scenarios, AM server 206 ranks the scenarios based uponcertainty or likelihood that the scenario is correct. The level ordegree of certainty is compared to a threshold, where AM server 206selects the scenario with the highest degree of certainty that exceedsthe threshold. In other embodiments, AM server 206 may model differentparts of the accident based upon the available sensor data. For example,AM server 206 may only have partial sensor data and may only be able torecreate a portion of the accident scene. In the example embodiment, AMserver 206 may use machine-learning to generate a model of the crashbased upon the sensor data and the known vehicular accident scenarios.

In the exemplary embodiment, if AM server 206 matches 216 a scenario, AMserver 206 stores 218 the matched scenario and may transmit the matchedscenario to user device 204. In this embodiment, user device 204 may bea computer device that is separate from the mobile device thattransmitted 210 the sensor data. In this embodiment, separate userdevice 204 may be registered with AM server 206. AM server 206 maytransmit a link to matched scenario to user via an email or applicationthat the user logs into. User device 204 depicts 220 the scenario touser 202 for user 202 to validate. If the scenario is correct 222, thenuser 202 inputs a confirmation on user device 204 and user device 204transmits the confirmation to AM server 206. If the scenario is closeand just needs changes, then user device 204 presents 224 a screen toallow user 202 to input changes 226 to the scenario. User device 204transmits 228 the updated scenario to AM server 206. AM server 206stores the updates. If the scenario was incorrect, user device 204 maypresent 236 an accident scene creation interface to user 202 that allowsuser 202 to create the scenario.

If the scenario may have caused an injury 230, AM server 206 identifies232 potential injuries that may have occurred during the accident basedupon the scenario. In some embodiments, the received sensor data mayinclude occupant sensor data that identifies a location and/or aposition of one or more occupants. AM server 206 may be configured toidentify 232 potential injuries based upon the location and/or positionof the occupant (e.g., a front seat passenger facing the back seatduring the crash may suffer potential back and spinal injuries). AMserver 206 prompts user device 204 to display 234 the identifiedinjuries to user 202 or to inquire if any injuries incurred. If the userknows of any injuries 238, user device 204 confirms or updates 240 theinjuries section of the model or scenario and transmits to AM server206, which updates the scenario. Whether or not there were any injuries,AM server 206 stores 242 the completed scenario.

Exemplary Computer-Implemented Method for Reconstructing a VehicularCrash

FIG. 3 illustrates a flow chart of an exemplary computer implementedprocess 300 for reconstructing a vehicular crash as shown in FIG. 2.Process 300 may be implemented by a computing device, for example AMserver 206 (shown in FIG. 2). In the exemplary embodiment, AM server 206may be in communication with a mobile computer device 405 (shown in FIG.4), such as mobile device 125, vehicle controller 110 (both shown inFIG. 1), and user device 204 (shown in FIG. 2).

In the exemplary embodiment, AM server 206 may receive 305 sensor dataof a vehicular crash from at least one mobile computer device 405associated with a user, such as user 202 (shown in FIG. 2). In theexemplary embodiment, user 202 is an insurance policyholder associatedwith at least one vehicle 100 (shown in FIG. 1) that is involved in avehicular crash or accident. In some embodiments, mobile computer device405 may be mobile device 125 shown in FIG. 1. In these embodiments, userdevice 204 may be a smart phone or other computer device associated withuser 202. User 202 may be driver 115 or passenger 120 of vehicle 100.

In the exemplary embodiment, mobile computer device 405 receives thesensor data from at least one sensor 410 associated with mobile computerdevice 405. In some embodiments, at least one sensor 410 may be one ormore of plurality of sensors 105 (shown in FIG. 1) in vehicle 100. Inother embodiments, at least one sensor 410 may be an accelerometer orother sensor in mobile device 125. In the exemplary embodiment, thesensor data provides information about the vehicular crash. Thisinformation includes, but is not limited to, vehicular conditions priorto, during, and after the accident or crash (i.e., speed, acceleration,location, direction of travel, braking, cornering, information aboutsurroundings, and operating conditions), weight distributioninformation, forces and directions of forces experienced and/or receivedduring the accident (i.e., changes in speed and direction), informationabout the occupants of vehicle 100 (i.e., number, weight, location,position, and seatbelt status), details about vehicle 100 (i.e., make,model, and mileage), and any actions that vehicle 100 took during theaccident (i.e., airbag deployment). In some embodiments, sensor data mayinclude data for a period of time prior to the vehicular accident andcontinue through a period of time after the vehicular accident. In somescenarios, where mobile device 125 may be loose in vehicle 100, mobiledevice 125 may be ejected through a windshield or out through a sidewindow. In these scenarios, AM server 206 may determine that mobiledevice 125 has left vehicle 100 and is no longer providing data aboutvehicle 100. In these scenarios, AM server 206 may determine the pointthat mobile device 125 exited vehicle 100 and not use data from mobiledevice 125 after that point.

In some embodiments, vehicle controller 110 collects the sensor datafrom sensors 105 and transmits the sensor data to AM server 206. Inother embodiments, mobile device 125 transmits its collected sensor datato AM server 206. In still other embodiments, mobile device 125 is incommunication with vehicle controller 110. In these other embodiments,mobile device 125 transmits its collected sensor data to vehiclecontroller 110 and vehicle controller 110 transmits the sensor data frommobile device 125 and from vehicle's sensors 105 to AM server 206.

In the exemplary embodiment, AM server 206 generates 310 a scenariomodel of the vehicular crash based upon the received sensor data.Scenario models may predict damage to vehicle 100 and injuries that maybe experiences by driver 115 and passengers 120 of vehicle 100. In oneembodiment, the received sensor data may include sensor data fromoccupant position sensors that identify a location and/or a position ofeach occupant within vehicle 100 prior to or at the time of thevehicular crash. The location and/or position of each occupant may beused to determine what forces were applied to an occupant's joints andskeletal structure during the crash and identify any correspondingpotential injuries. For example, a front row passenger 120 turningaround to help a child in the back of vehicle 100 may injure his or herback during the vehicular crash because his or her position at impactwould not protect his or her body from injury. The sensor data from theoccupant position sensors may be combined with data indicating theposition of support structures of vehicle 100 (e.g., frame, windows,seats, steering wheel, etc.) to identify any potential injuries that anoccupant may have sustained due to the occupant's position relative tothe positions of the support structures. In the exemplary embodiment, AMserver 206 accesses a database, such as database 420 (shown in FIG. 4).Database 420 may contain a plurality of crash scenarios and the sensordata associated with these crash scenarios. The scenarios may be basedupon information from vehicle crash testing facilities, from pastcrashes that AM server 206 has analyzed, and/or from other sources thatallow AM server 206 to operate as described here. AM server 206 comparesthe received sensor data with the different stored crash scenarios togenerate 310 a scenario model that is the most likely match for thevehicular crash. For example, AM server 206 may determine that vehicle100 was rear-ended by another vehicle that was going approximately 30miles an hour while vehicle 100 was stopped.

In some embodiments, AM server 206 generates a plurality of scenariomodels that may fit the sensor data received. AM server 206 may thenrank the generated scenarios based upon the likelihood or degree ofcertainty that the scenario is correct. In some further embodiments, AMserver 206 may compare the degree of certainty to a predeterminedthreshold.

AM server 206 transmits 315 the scenario model to a computer deviceassociated with user 202, such as user device 204. In some embodiments,user device 204 is associated with the policyholder of an accountassociated with vehicle 100. In other embodiments, user device 204 isassociated with one of the occupants of vehicle 100. In still otherembodiments, user device 204 is associated with a third party, such asone of the emergency service personnel.

User device 204 may be configured to display the scenario model to user202. User device 204 may be configured to receive input from user 202about the displayed scenario model. In some embodiments, user device 204may receive a confirmation from user 202 that the scenario model iscorrect. User device 204 may transmit the confirmation for the scenariomodel to AM server 206 (such as via wireless communication or datatransmission over one or more radio frequency links or wirelesscommunication channels). AM server 206 may receive 320 the confirmation.

AM server 206 stores 325 the scenario model. Based upon the storedscenario model for the vehicular crash, AM server 206 generates 330 atleast one insurance claim form for the vehicular accident. In theexemplary embodiment, AM server 206 retrieves one or more stored blankclaim forms, such as from database 420. AM server 206 may then populateone or more of the blank fields of the retrieved blank forms based uponthe scenario model. Based upon the completeness of the stored scenariomodel, AM server 206 may be able to fill out multiple forms and describethe accident in detail. AM server 206 may also be able to determine oneor more damages that vehicle 100 would have incurred in the vehicularaccident. AM server 206 may also be able to estimate a cost of repairsor replacement.

In some embodiment, AM server 206 may add the stored scenario model andthe associated sensor data to database 420 to improve the stored models.In these embodiments, AM server 206 may be executing one or more machinelearning algorithms to improve the accuracy of the generating scenariomodels.

In other embodiments, user device 204 may receive changes and/or updatesto the scenario model from user 202. AM server 206 may update thescenario model based upon the updates received from user 202.

In some embodiments, AM server 206 may be able to determine one or morepotential injuries to one or more occupants of the vehicle based uponthe scenario model. For example, in the rear-end accident example AMserver 206 may determine that there is a 40% chance that driver 115 mayhave incurred a minor neck injury. In one embodiment, AM server 206 maybe configured to determine one or more injuries to the occupants basedupon the position of the occupants relative to the vehicle and/or thesupport structures of the vehicle. AM server 206 may then transmit theone or more potential injuries to user device 204 for confirmation byuser 202. Based upon which injuries that user 202 indicates whereincurred, AM server 206 then may update the scenario model.

In some further embodiments, AM server 206 may receive sensor data frommore than one vehicle involved in the vehicular accident. AM server 206may then combine the sensor data from the multiple vehicles to updatethe scenario model. AM server 206 may also expand the scenario model forthe occupants of each other vehicle involved in the vehicular accident.

In some further embodiments, AM server 206 may be in communication withone or more emergency services providers, such as a towing service, anemergency medical service provider, a police department, a firedepartment, and/or some other emergency responder. AM server 206 maycontact one or more of these emergency service providers based upon thescenario model and the location of the vehicular crash. For example, inthe rear-end example AM server 206 may contact the nearest emergencymedical service provider and request immediate deployment for treatmentof a potential neck injury on driver 115. AM server 206 may also provideother information from the scenario model to the contacted emergencyservice provider.

In some embodiments, AM server 206 may determine a severity of thevehicular accident. The determined severity may be based upon aplurality of levels of severity, such as set by government or astandards setting organization. Examples may include, but are notlimited to, vehicle damage scale, damage severity code, and injuryseverity score. AM server 206 may transmit the determined severity tothe one or more emergency services providers. AM server 206 may also usethe determined severity in generating the claim forms.

In some embodiments, the state of connectivity of each sensor 105 invehicle 100, each mobile device 125 in vehicle 100, and vehiclecontroller 110 are tracked by AM server 206. In some embodiments, thestate of connectivity is used in validating the data stream. In otherembodiments, the state of connectivity is used in determining theseverity of the vehicular accident. In these embodiments, AM server 206determines the extent of trauma required to induce damage to sensor 105,mobile device 125, or vehicle controller 110 severe enough to sever theconnection to the corresponding item in relation to the point of impact.For example, if the scenario is for a driver's side impact and a mobiledevice 125 in driver's pocket is destroyed, then severity is likelylevel x. Other scenarios may include, but are not limited to, front(left/right) impact and battery connection to ECU severed or head-onimpact and Bluetooth module in dashboard destroyed.

Exemplary Computer Network

FIG. 4 depicts a simplified block diagram of an exemplary system 400 forimplementing process 200 shown in FIG. 2. In the exemplary embodiment,system 400 may be used for reconstructing the vehicular accident basedupon sensor data, confirming the scenario with user 202 (shown in FIG.2), and populating one or more claims forms. As described below in moredetail, accident monitoring (“AM”) server 206 (shown in FIG. 2) may beconfigured to receive sensor data of a vehicular crash from at least onemobile device 204 (shown in FIG. 2) associated with user 202, generate ascenario model of the vehicular crash based upon the received sensordata, transmit the scenario model to a computer device associated withuser 202, receive a confirmation of the scenario model from the computerdevice associated with user 202, store the scenario model, and/orgenerate at least one insurance claim form based upon the scenario modelto facilitate quickly and accurately processing an insurance claim.

In the exemplary embodiment, user computer devices 425 are computersthat include a web browser or a software application, which enables usercomputer devices 425 to access AM server 206 using the Internet or othernetwork. More specifically, user computer devices 425 arecommunicatively coupled to the Internet through many interfacesincluding, but not limited to, at least one of a network, such as theInternet, a local area network (LAN), a wide area network (WAN), or anintegrated services digital network (ISDN), a dial-up-connection, adigital subscriber line (DSL), a cellular phone connection, and a cablemodem. User computer devices 425 may be any device capable of accessingthe Internet including, but not limited to, a desktop computer, a laptopcomputer, a personal digital assistant (PDA), a cellular phone, asmartphone, a tablet, a phablet, wearable electronics, smart watch, orother web-based connectable equipment or mobile devices. In someembodiments, user computer device 425 is associated with thepolicyholder of an account associated with vehicle 100. In otherembodiments, user computer device 425 is associated with one of theoccupants of vehicle 100. In still other embodiments, user computerdevice 425 is associated with a third party, such as one of theemergency service personnel.

A database server 410 may be communicatively coupled to a database 420that stores data. In one embodiment, database 420 may include vehicularcrash scenarios, sensor data, and/or insurance claim forms. In theexemplary embodiment, database 420 may be stored remotely from AM server206. In some embodiments, database 420 may be decentralized. In theexemplary embodiment, user 202 may access database 420 via user computerdevices 405 by logging onto AM server 206, as described herein.

AM server 206 may be communicatively coupled with the user computerdevices 425. In some embodiments, AM server 206 may be associated with,or is part of a computer network associated with an insurance provider,or in communication with the insurance provider's computer network (notshown). In other embodiments, AM server 206 may be associated with athird party and is merely in communication with the insurance provider'scomputer network.

One or more mobile computer devices 405 may be communicatively coupledwith AM server 206 through the Internet or a cellular network. In theexemplary embodiment, mobile computer devices 405 are computers thatinclude a software application, which enables mobile computer devices405 to access AM server 206 using the Internet or other network. Morespecifically, mobile computer devices 405 are communicatively coupled tothe Internet through many interfaces including, but not limited to, atleast one of a network, such as the Internet, a local area network(LAN), a wide area network (WAN), or an integrated services digitalnetwork (ISDN), a dial-up-connection, a digital subscriber line (DSL), acellular phone connection, and a cable modem.

Mobile computer devices 405 may also include one or more sensors 410.Mobile computer devices 405 may be configured to receive data fromsensors 410 and transmit sensor data to AM server 206. In someembodiments, mobile computer device 405 may be mobile device 125associated with one of the occupants of vehicle 100. Mobile computerdevice 405 may be, but is not limited to, a personal digital assistant(PDA), a cellular phone, a smartphone, a tablet, a phablet, wearableelectronics, smart watch, or other web-based connectable equipment ormobile devices that allow them to function as described herein. In otherembodiments, mobile computer device 405 is vehicle 100, and morespecifically, vehicle controller 110 (shown in FIG. 1). In some of theseembodiments, vehicle controller 110 is in communication with a secondmobile computer device 405, such as a user computer device 425. In theseembodiments, vehicle controller 110 may be configured to receive sensordata from user computer device 425 and transmit the sensor data to AMserver 206.

In the exemplary embodiment, sensor 410 may be a configured to detectone or more conditions about vehicle 100. For example, sensor 410 may besensor 105 (shown in FIG. 1). In other embodiments, sensor 410 may beconfigured to detect one or more conditions of one or more occupants ofvehicle 100, such as driver 115 and/or passengers 120 (both shown inFIG. 1).

Exemplary Client Device

FIG. 5 depicts an exemplary configuration of user computer device 425shown in FIG. 4, in accordance with one embodiment of the presentdisclosure. User computer device 502 may be operated by a user 501. Inthe exemplary embodiment, user 501 may be similar to user 202 (shown inFIG. 2). User computer device 502 may include, but is not limited to,user computer devices 425 (shown in FIG. 4), mobile computer device 405(shown in FIG. 4), vehicle controller 110 (shown in FIG. 1), mobiledevice 125 (shown in FIG. 1), and user device 204 (shown in FIG. 2).User computer device 502 may include a processor 505 for executinginstructions. In some embodiments, executable instructions are stored ina memory area 510. Processor 505 may include one or more processingunits (e.g., in a multi-core configuration). Memory area 510 may be anydevice allowing information such as executable instructions and/ortransaction data to be stored and retrieved. Memory area 510 may includeone or more computer readable media.

User computer device 502 may also include at least one media outputcomponent 515 for presenting information to user 501. Media outputcomponent 515 may be any component capable of conveying information touser 501. In some embodiments, media output component 515 may include anoutput adapter (not shown) such as a video adapter and/or an audioadapter. An output adapter may be operatively coupled to processor 505and operatively coupleable to an output device such as a display device(e.g., a cathode ray tube (CRT), liquid crystal display (LCD), lightemitting diode (LED) display, or “electronic ink” display) or an audiooutput device (e.g., a speaker or headphones).

In some embodiments, media output component 515 may be configured topresent a graphical user interface (e.g., a web browser and/or a clientapplication) to user 501. A graphical user interface may include, forexample, an online store interface for viewing and/or purchasing items,and/or a wallet application for managing payment information. In someembodiments, user computer device 502 may include an input device 520for receiving input from user 501. User 501 may use input device 520 to,without limitation, select and/or enter one or more items to purchaseand/or a purchase request, or to access credential information, and/orpayment information.

Input device 520 may include, for example, a keyboard, a pointingdevice, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad ora touch screen), a gyroscope, an accelerometer, a position detector, abiometric input device, and/or an audio input device. A single componentsuch as a touch screen may function as both an output device of mediaoutput component 515 and input device 520.

User computer device 502 may also include a communication interface 525,communicatively coupled to a remote device such as AM server 206 (shownin FIG. 2) or vehicle controller 110. Communication interface 525 mayinclude, for example, a wired or wireless network adapter and/or awireless data transceiver for use with a mobile telecommunicationsnetwork.

Stored in memory area 510 are, for example, computer readableinstructions for providing a user interface to user 501 via media outputcomponent 515 and, optionally, receiving and processing input from inputdevice 520. A user interface may include, among other possibilities, aweb browser and/or a client application. Web browsers enable users, suchas user 501, to display and interact with media and other informationtypically embedded on a web page or a website from AM server 206. Aclient application allows user 501 to interact with, for example, AMserver 206. For example, instructions may be stored by a cloud service,and the output of the execution of the instructions sent to the mediaoutput component 515.

Processor 505 executes computer-executable instructions for implementingaspects of the disclosure. In some embodiments, the processor 505 istransformed into a special purpose microprocessor by executingcomputer-executable instructions or by otherwise being programmed. Forexample, the processor 505 may be programmed with the instruction suchas illustrated in FIG. 8.

In some embodiments, user computer device 502 may include, or be incommunication with, one or more sensors, such as sensor 105 (shown inFIG. 1) and sensor 410 (shown in FIG. 4). User computer device 502 maybe configured to receive data from the one or more sensors and store thereceived data in memory area 510. Furthermore, user computer device 502may be configured to transmit the sensor data to a remote computerdevice, such as AM server 206, through communication interface 525.

Exemplary Server Device

FIG. 6 depicts an exemplary configuration of server 206 shown in FIG. 4,in accordance with one embodiment of the present disclosure. Servercomputer device 601 may include, but is not limited to, database server415 (shown in FIG. 4), AM server 206, and vehicle controller 110 (shownin FIG. 1). Server computer device 601 may also include a processor 605for executing instructions. Instructions may be stored in a memory area610. Processor 605 may include one or more processing units (e.g., in amulti-core configuration).

Processor 605 may be operatively coupled to a communication interface615 such that server computer device 601 is capable of communicatingwith a remote device such as another server computer device 601, mobiledevice 125 (shown in FIG. 1), mobile computer device 405 (shown in FIG.4), user computer device 425 (shown in FIG. 4), and AM server 206. Forexample, communication interface 615 may receive requests from usercomputer devices 425 via the Internet, as illustrated in FIG. 4.

Processor 605 may also be operatively coupled to a storage device 634.Storage device 634 may be any computer-operated hardware suitable forstoring and/or retrieving data, such as, but not limited to, dataassociated with database 420 (shown in FIG. 4). In some embodiments,storage device 634 may be integrated in server computer device 601. Forexample, server computer device 601 may include one or more hard diskdrives as storage device 634.

In other embodiments, storage device 634 may be external to servercomputer device 601 and may be accessed by a plurality of servercomputer devices 601. For example, storage device 634 may include astorage area network (SAN), a network attached storage (NAS) system,and/or multiple storage units such as hard disks and/or solid statedisks in a redundant array of inexpensive disks (RAID) configuration.

In some embodiments, processor 605 may be operatively coupled to storagedevice 634 via a storage interface 620. Storage interface 620 may be anycomponent capable of providing processor 605 with access to storagedevice 634. Storage interface 620 may include, for example, an AdvancedTechnology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, aSmall Computer System Interface (SCSI) adapter, a RAID controller, a SANadapter, a network adapter, and/or any component providing processor 605with access to storage device 634.

Processor 605 may execute computer-executable instructions forimplementing aspects of the disclosure. In some embodiments, theprocessor 605 may be transformed into a special purpose microprocessorby executing computer-executable instructions or by otherwise beingprogrammed. For example, the processor 605 may be programmed with theinstruction such as illustrated in FIGS. 3 and 7.

Exemplary Computer-Implemented Method for Notifying Emergency Servicesof a Vehicular Crash

FIG. 7 illustrates a flow chart of an exemplary computer implementedprocess 700 for notifying emergency services of a vehicular crash usingsystem 400 shown in FIG. 4. Process 700 may be implemented by acomputing device, for example AM server 206 (shown in FIG. 2). In theexemplary embodiment, AM server 206 may be in communication with amobile computer device 405 (shown in FIG. 4), such as mobile device 125,vehicle controller 110 (both shown in FIG. 1), and user device 204(shown in FIG. 2).

In the exemplary embodiment, AM server 206 may receive 705 sensor dataof a vehicular crash from at least one mobile computer device 405associated with a user, such as user 202 (shown in FIG. 2). In theexample embodiment, user 202 is an insurance policyholder associatedwith at least one vehicle 100 (shown in FIG. 1) that is involved in avehicular crash or accident. In some embodiments, mobile computer device405 may be mobile device 125 shown in FIG. 1. In these embodiments, userdevice 204 may be a smart phone or other computer device associated withuser 202. User 202 may be driver 115 or passenger 120 of vehicle 100.

In the exemplary embodiment, mobile computer device 405 receives thesensor data from at least one sensor 410 associated with mobile computerdevice 405. In some embodiments, at least one sensor may be one or moreof plurality of sensors 105 (shown in FIG. 1) in vehicle 100. In otherembodiments, at least one sensor 410 may be an accelerometer, gyroscope,compass, or other sensor in mobile device 125. In the exemplaryembodiment, the sensor data provides information about the vehicularcrash. This information includes, but is not limited to, vehicularconditions prior to, during, and after the accident or crash (i.e.,speed, acceleration, location, direction of travel, information aboutsurroundings, and operating conditions), weight distributioninformation, forces and directions of forces experienced and/or receivedduring the accident (i.e., changes in speed and direction), informationabout the occupants of vehicle 100 (i.e., number, weight, location,position, and seatbelt status), details about vehicle 100 (i.e., make,model, and mileage), and any actions that vehicle 100 took during theaccident (i.e., airbag deployment). In some embodiments, sensor data mayinclude data for a period of time prior to the vehicular accident andcontinue through a period of time after the vehicular accident.

In some embodiments, vehicle controller 110 collects the sensor datafrom sensors 105 and transmits the sensor data to AM server 206. Inother embodiments, mobile device 125 transmits its collected sensor datato AM server 206. In still other embodiments, mobile device 125 is incommunication with vehicle controller 110. In these other embodiments,mobile device 125 transmits its collected sensor data to vehiclecontroller 110 and vehicle controller 110 transmits the sensor data frommobile device 125 and from vehicle's sensors 105 to AM server 206.

In the exemplary embodiment, AM server 206 generates 710 a scenariomodel of the vehicular crash based upon the received sensor data. In theexemplary embodiment, AM server 206 accesses a database, such asdatabase 420 (shown in FIG. 4). Database 420 may contain a plurality ofcrash scenarios and the sensor data associated with these crashscenarios. The scenarios may be based upon information from vehiclecrash testing facilities, from past crashes that AM server 206 hasanalyzed, and/or from other sources that allow AM server 206 to operateas described herein. AM server 206 compares the received sensor datawith the different stored crash scenarios to generate 710 a scenariomodel that is the most likely match for the vehicular crash. Forexample, AM server 206 may determine that vehicle 100 was rear-ended byanother vehicle that was going approximately 30 miles an hour whilevehicle 100 was stopped.

In some embodiments, AM server 206 may generate a plurality of scenariomodels that may fit the sensor data received. AM server 206 may thenrank the generated scenarios based upon the likelihood or degree ofcertainty that the scenario is correct. In some further embodiments, AMserver 206 may compare the degree of certainty to a predeterminedthreshold.

AM server 206 may store 715 the scenario model. In the exemplaryembodiment, AM server 206 may be in communication with one or moreemergency services providers, such as a towing service, an emergencymedical service provider, a police department, a fire department, and/orsome other emergency responder. AM server 206 may transmit 720 a messageabout the vehicular accident to one or more of these emergency serviceproviders based upon the scenario model and the location of thevehicular crash. For example, in the rear-end example AM server 206 maycontact the nearest emergency medical service provider and requestimmediate deployment for treatment of a potential neck injury on driver115. AM server 206 may also provide other information from the scenariomodel to the contacted emergency service provider.

In some embodiments, AM server 206 may be able to determine one or morepotential injuries to one or more occupants of the vehicle based uponthe scenario model. For example, in the rear-end accident example AMserver 206 may determine that there is a 40% chance that driver 115 mayhave incurred a minor neck injury. In some embodiments, AM server 206may determine potential injuries have occurred to an occupant based uponthe occupant's position relative to vehicle 100 and/or the supportstructures of vehicle 100. AM server 206 may then transmit the one ormore potential injuries to user device 204 for confirmation by user 202.Based upon which injuries that user 202 indicates where incurred, AMserver 206 then may update the scenario model.

In some embodiments, AM server 206 may transmit a message to user 202that emergency personnel have been contacted and are on their way. Themessage may further instruct user 202 wait to be checked by emergencypersonnel. For example, user 202 may feel fine, but since AM server 206determined there is a chance that user 202 has incurred an injury, AMserver 206 may request that user 202 wait to be checked out rather thanwaving off help.

Based upon the stored scenario model for the vehicular crash, AM server206 generates 330 at least one insurance claim form for the vehicularaccident. In the exemplary embodiment, AM server 206 retrieves one ormore stored blank claim forms, such as from database 420. AM server 206may then populate one or more of the blank fields of the retrieved blankforms based upon the scenario model. Based upon the completeness of thestored scenario model, AM server 206 may be able to fill out multipleforms and describe the accident in detail. AM server 206 may also beable to determine one or more damages that vehicle 100 would haveincurred in the vehicular accident. AM server 206 may also be able toestimate a cost of repairs or replacement.

In some embodiment, AM server 206 may add the stored scenario model andthe associated sensor data to database 420 to improve the stored models.In these embodiments, AM server 206 may be executing one or more machinelearning algorithms to improve the accuracy of the generating scenariomodels.

In some further embodiments, AM server 206 may receive sensor data frommore than one vehicle involved in the vehicular accident. AM server 206may then combine the sensor data from the multiple vehicles to updatethe scenario model. AM server 206 may also expand the scenario model forthe occupants of each other vehicle involved in the vehicular accident.

In some embodiments, mobile device 125 may be able to determine that itis currently in a pocket of user 202. In these embodiments, AM server206 may be able to determine the exact amounts of force and directionsof force that were exerted on user 202 during the vehicular accident.Based upon this information, AM server 206 may be able to moreaccurately determine the potential injuries received by user 202 andnotify emergency personnel of those potential injuries.

In some embodiments, AM server 206 may determine a severity of thevehicular accident. The determined severity may be based upon aplurality of levels of severity, such as set by government or astandards setting organization. Examples may include, but are notlimited to, vehicle damage scale, damage severity code, and injuryseverity score. AM server 206 may transmit the determined severity tothe one or more emergency services providers.

In some embodiments, the state of connectivity of each sensor 105 invehicle 100, each mobile device 125 in vehicle 100, and vehiclecontroller 110 are tracked by AM server 206. In some embodiments, thestate of connectivity is used in validating the data stream. In otherembodiments, the state of connectivity is used in determining theseverity of the vehicular accident. In these embodiments, AM server 206determines the extent of trauma required to induce damage to sensor 105,mobile device 125, or vehicle controller 110 severe enough to sever theconnection to the corresponding item in relation to the point of impact.For example, if the scenario is for a driver's side impact and a mobiledevice 125 in driver's pocket is destroyed, then severity is likelylevel x. Other scenarios may include, but are not limited to, front(left/right) impact and battery connection to ECU severed or head-onimpact and Bluetooth module in dashboard destroyed.

Exemplary Computer-Implemented Method for Detecting a Vehicular Crash

FIG. 8 illustrates a flow chart of an exemplary computer implementedprocess 800 for detecting a vehicular crash using system 400 shown inFIG. 4. Process 800 may be implemented by a computing device, forexample mobile computer device 405 (shown in FIG. 4). In the exemplaryembodiment, mobile computer device 125 may be in communication with AMserver 206 (shown in FIG. 2). In a first exemplary embodiment, mobilecomputer device 405 may be vehicle controller 110 in vehicle 100 (bothshown in FIG. 1). In a second exemplary embodiment, mobile computerdevice 405 may be mobile device 125 (shown in FIG. 1).

In the exemplary embodiment, mobile computer device 405 receives 805data from at least one sensor 410 (shown in FIG. 4). In the firstexemplary embodiment, at least one sensor 410 may be one or more ofplurality of sensors 105 (shown in FIG. 1) in vehicle 100. In the secondexemplary embodiment, the at least one sensor 410 may be anaccelerometer in mobile device 125.

Mobile computer device 405 determines 810 that a potential vehicularcrash is imminent based upon the received sensor data. For example, inthe first exemplary embodiment, sensor 105 is an external sensor and mayshow that another vehicle is about to collide with vehicle 100. Orsensor 105 may be an impact sensor or any other sensor that allowsmobile computer device 405 to work as described herein. In the secondexemplary embodiment, mobile device 125 may have been loose in vehicle,such as resting on the dashboard or sitting in a cup holder when theaccident occurred. The data from the accelerometer may indicate thatmobile device 125 is airborne. Or mobile device 125 may have been in apocket of driver 115 or passenger 120 (both shown in FIG. 1). The datafrom accelerometer may indicate that forces on mobile device 125indicate that an accident may be occurring. In some scenarios, wheremobile device 125 may be loose in vehicle 100, mobile device 125 may beejected through a windshield or out through a side window. In thesescenarios, AM server 206 may determine that mobile device 125 has leftvehicle 100 and is no longer providing data about vehicle 100. In thesescenarios, AM server 206 may determine the point that mobile device 125exited vehicle 100 and not use data from mobile device 125 after thatpoint.

Mobile computer device 405 transmits 815 one or more high prioritypackets to AM server 206 indicating that a vehicular crash may beimminent or may be currently occurring. Mobile computer device 405 mayinstruct communication interface 525 (shown in FIG. 5) to override anycurrent communication to transmit 815 the high priority packets. Forexample, mobile computer device 405 may transmit 815 the high prioritypackets to indicate the crash before mobile computer device 405 isrendered inoperable or incapable of transmitting data due to thevehicular accident. In some embodiments, mobile computer device 405 mayalso transmit at least part of the received sensor data. Mobile computerdevice 405 may transmit 815 priority data for as long as it is able.

In one exemplary embodiment, the first high priority packet transmittedindicates that a crash is potentially imminent or currently occurring.Based upon that packet, AM server 206 knows to listen for any sensordata or a broadcast of sensor data. Furthermore, the receipt of the highpriority packet may be the indication that an accident has been detected208 (shown in FIG. 2). Any packets that mobile computer device 405transmits after the first packet may contain sensor data. The first highpriority packet may also instruct AM server 206 to not transmit and toonly listen for packets. Mobile computer device 405 may be configured toonly transmit 815 high priority packets after determining 810 that anaccident may be occurring or about to occur. Mobile computer device 405may also be configured to not allow any other communication channel tobe used for a period of time after the accident is detected 208. Thisrestriction may be to allow as much sensor data to be uploaded asquickly as possible. After a period of time, mobile computer device 405may allow other communications to be placed, such as phone calls toemergency services.

Exemplary Vehicular Crash Detection

FIG. 9 illustrates a flow chart of another exemplarycomputer-implemented process 900 of detecting a vehicular crash usingsystem 400 (shown in FIG. 4). Process 900 may be implemented by acomputing device, for example vehicle computer device 110 (shown in FIG.1). In some embodiments, parts of process 900 may be implemented by AMserver 415 (shown in FIG. 2). In the exemplary embodiment, vehiclecomputer device 110 may be in communication with AM server 415.

In the exemplary embodiment, vehicle computer device 110 receives 905data from at least one internal facing sensor 105 (shown in FIG. 1). Inthe exemplary embodiment, at least one internal facing sensor 105 may beone or more of plurality of sensors 105 (shown in FIG. 1) in vehicle 100(shown in FIG. 1). Internal facing sensors 105 may include cabin-facingsensors that may be configured to collect sensor data associated withoccupants (i.e., a driver 115 and a passenger 120 (both shown in FIG.1)) and/or luggage within the vehicle, such as a location and/or aposition of the occupants. The location of an occupant may refer to aparticular seat or other portion of the vehicle where the occupant islocated. The position of an occupant may refer to a body and/or limborientation of an occupant relative to the vehicle or components of thevehicle, such as a steering wheel, a front portion of the vehicle, andthe like. In some embodiments, the sensors may be configured to collectsensor data associated with weight distribution information of thevehicle, occupants, luggage, fuel, and so forth. In these embodiments,plurality of sensors 105 may detect the presence of fastened seatbelts,the weight in each seat in vehicle 100, heat signatures, distinctvoices, image recognition, weight on each wheel, which TV monitors areactive, cameras, phone detection, sensors from one or more mobiledevices 125 (shown in FIG. 1), and/or any other method of detectinginformation about driver 115 and passengers 120 in vehicle 100.

In the exemplary embodiment, vehicle computer device 110 receives 910data from at least one external sensor 105 (shown in FIG. 1). In theexemplary embodiment, at least one external sensor 105 may be one ormore of plurality of sensors 105 (shown in FIG. 1) in vehicle 100. Theplurality of external sensors 105 may detect the current surroundingsand location of vehicle 100. Plurality of external sensors 105 mayinclude, but are not limited to, radar, LIDAR, Global Positioning System(GPS), video devices, imaging devices, cameras, audio recorders, andcomputer vision. Plurality of external sensors 105 may also includesensors that detect conditions of vehicle 100, such as velocity,acceleration, gear, braking, and other conditions related to theoperation of vehicle 100.

Vehicle computer device 110 determines 915 that a potential vehicularcrash is imminent based upon the received external sensor data. Forexample, in the exemplary embodiment, external sensor 105 is an externalsensor and may show that another vehicle is about to collide withvehicle 100. Or external sensor 105 may be an impact sensor or any othersensor that allows vehicle computer device 110 to work as describedherein.

Vehicle computer device 110 determines 920 positional information for atleast one occupant of vehicle 100. Positional information may include aposition of an occupant, a direction of facing of the occupant, a sizeof the occupant, and/or a skeletal positioning of the occupant. Theposition of the occupant may include which seat the occupant occupies.The direction of facing of the occupant may include whether the occupantis facing forward, reaching forward, reaching to the side, and/or hashis/her head turned. The size of the occupant may determine whether theoccupant is an adult or a child. The size of the occupant may alsoinclude the occupant's height. The skeletal positioning may includepositioning of the occupant's joints, spine, arms, legs, torso, neckface, head, major bones, hands, and/or feet. In some embodiments, theinternal sensors 105 constantly transmit sensor data to vehicle computerdevice 110, which constantly determines 920 the positional informationof the occupants. In other embodiments, vehicle computer device 110transmits the internal sensor data to AM server 415, which determines920 the positional information and transmits that information to vehiclecomputer device 110.

In some embodiments, vehicle computer device 110 generates a scenariomodel of the potential vehicular crash based upon the received externaland/or internal sensor data. Scenario models may predict damage tovehicle 100 and injuries that may be experiences by driver 115 andpassengers 120 of vehicle 100. In the exemplary embodiment, vehiclecomputer device 110 accesses a database, such as database 202 (shown inFIG. 2). Database 202 may contain a plurality of crash scenarios and thesensor data associated with these crash scenarios. The scenarios may bebased upon information from vehicle crash testing facilities, from pastcrashes that AM server 415 has analyzed, and/or from other sources thatallow vehicle computer device 110 to operate as described herein.Vehicle computer device 110 compares the received sensor data with thedifferent stored crash scenarios to generate a scenario model that isthe most likely match for the imminent vehicular crash. In some furtherembodiments, vehicle computer device 110 may communicate the sensor datato AM server 415, where AM server 415 may generate the scenario model.In the some of these embodiments, vehicle computer device 110 determinesone or more potential injuries to one or more occupants of vehicle 100based upon the positional information and the scenario model. Vehiclecomputer device 110 may also determine a severity for each potentialinjury.

In some embodiments, vehicle computer device 110 generates a pluralityof scenario models that may fit the sensor data received. Vehiclecomputer device 110 may then rank the generated scenarios based upon thelikelihood or degree of certainty that the scenario is correct. In somefurther embodiments, vehicle computer device 110 may compare the degreeof certainty to a predetermined threshold.

In the exemplary embodiment, vehicle computer device 110 performs 925 atleast one action to reduce a severity of a potential injury to at leastone occupant prior to impact. Using the scenario model, vehicle computerdevice 110 may be able to determine an advantageous direction of facingfor the at least one occupant. Vehicle computer device 110 may thengenerate a sound through the audio system of vehicle 100, such a horn oralarm sound. The sound would be generated to cause the at least oneoccupant to change to the advantageous direction of facing. For example,vehicle computer device 110 may generate a honking sound to cause thepassenger to turn around to prevent or reduce potential injuries duringthe imminent vehicular crash. Additionally or alternatively, vehiclecomputer device 110 may select and engage one or more autonomous orsemi-autonomous vehicle features or systems in an attempt to avoid ormitigate the vehicle collision.

The types of autonomous or semi-autonomous vehicle-related functionalityor technology that may be used with the present embodiments to replacehuman driver actions may include and/or be related to the followingtypes of functionality: (a) fully autonomous (driverless); (b) limiteddriver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d)vehicle-to-infrastructure (and/or vice versa) wireless communication;(e) automatic or semi-automatic steering; (f) automatic orsemi-automatic acceleration; (g) automatic or semi-automatic braking;(h) automatic or semi-automatic blind spot monitoring; (i) automatic orsemi-automatic collision warning; (j) adaptive cruise control; (k)automatic or semi-automatic parking/parking assistance; (l) automatic orsemi-automatic collision preparation (windows roll up, seat adjustsupright, brakes pre-charge, etc.); (m) driver acuity/alertnessmonitoring; (n) pedestrian detection; (o) autonomous or semi-autonomousbackup systems; (p) road mapping systems; (q) software security andanti-hacking measures; (r) theft prevention/automatic return; (s)automatic or semi-automatic driving without occupants; and/or otherfunctionality.

For the method discussed directly above, the wirelesscommunication-based autonomous or semi-autonomous vehicle technology orfunctionality may include and/or be related to: automatic orsemi-automatic steering; automatic or semi-automatic acceleration and/orbraking; automatic or semi-automatic blind spot monitoring; automatic orsemi-automatic collision warning; adaptive cruise control; and/orautomatic or semi-automatic parking assistance. Additionally oralternatively, the autonomous or semi-autonomous technology orfunctionality may include and/or be related to: driver alertness orresponsive monitoring; pedestrian detection; artificial intelligenceand/or back-up systems; navigation or GPS-related systems; securityand/or anti-hacking measures; and/or theft prevention systems.

In other embodiments, vehicle computer device 110 may be able todetermine an advantageous position for the at least one occupant.Vehicle computer device 110 may cause a seat to shift or move, such asadjusting the recline angle of the seat, to cause the occupant to changeto the advantageous position. Vehicle computer device 110 may alsorotate the seat of occupant to cause the occupant to change to theadvantageous position or advantageous facing.

In yet other embodiments, vehicle computer device 110, in addition to oralternatively to detecting a potential or actual vehicle collision, mayalso reconstruct a vehicle collision. For instance, vehicle computerdevice 110 may use the internal sensor data and/or external sensor datagenerated and/or collected to reconstruct passenger position within thevehicle, as well as vehicle speed and direction, prior to, during, andafter a vehicle collision or impact. The vehicle computer device 110 mayinclude additional, less, or alternate functionality, including thatdiscussed elsewhere herein, to reconstruct vehicle collisions.

In some further embodiments, sensors 105 may detect one or more looseobjects in the passenger cabin of vehicle 100. Examples of loose objectsinclude, but are not limited to, mobile electronics, purses and otherbags, toys, tissue boxes, trash, and other objects in the vehicle thatwould move during a vehicular collision. In these embodiments, AM server415 may include the one or more loose objects in the model scenario andmay predict one or more injuries based upon potential trajectories ofthe one or more loose objects.

For instance, in one embodiment, a computer system for reconstructing avehicle collision may be provided. The computer system may include oneor more processors, sensors, and/or transceivers in communication withat least one memory device. The one or more processors, sensors, and/ortransceivers may be programmed or otherwise configured to: (1) receiveoccupant data from at least one internal sensor, the occupant data beinggenerated or collected before, during, and/or after the vehiclecollision; (2) receive external data from the at least one externalsensor, the external data being generated or collected before, during,and/or after the vehicle collision; (3) determine positional informationfor at least one occupant of a vehicle before, during, and/or after thevehicle collision; and/or (4) generate a virtual reconstruction of thevehicle crash, the virtual reconstruction indicating a severity ofvehicle damage and a severity of a potential injury to the at least oneoccupant caused by the vehicle collision. The severity of vehicle damageand a severity of a potential injury to the at least one occupant causedby the vehicle collision may be determined or estimated from processoranalysis performed by the one or more processors of (i) the occupantdata being generated or collected before, during, and/or after thevehicle collision; (ii) the external data being generated or collectedbefore, during, and/or after the vehicle collision; and/or (iii) thepositional information for at least one occupant of a vehicle before,during, and/or after the vehicle collision.

The one or more processors may be further programmed to: determine aposition and a direction of facing of at least one occupant of thevehicle before, during, and/or after the vehicle collision based uponthe internal data; determine occupant skeletal positioning for the atleast one occupant before, during, and/or after the vehicle collisionbased upon the internal data; and/or determine a size of the at leastone occupant based upon the internal data.

In another embodiment, a computer-based method for reconstructing avehicle collision may be provided. The method may be implemented on avehicle computer device including one or more processors, sensors,and/or transceivers in communication with at least one memory device.The method may include, via the one or more processors, sensors, and/ortransceivers: (1) receiving occupant data from at least one internalsensor, the occupant data being generated or collected before, during,and/or after the vehicle collision; (2) receiving external data from theat least one external sensor, the external data being generated orcollected before, during, and/or after the vehicle collision; (3)determining, by the vehicle computer device, positional information forat least one occupant of a vehicle before, during, and/or after vehiclecollision; and/or (4) generating a virtual reconstruction of the vehiclecollision, the virtual reconstruction indicating a severity of damage tothe vehicle and a severity of a potential injury to the at least oneoccupant caused by the vehicle collision. The severity of vehicle damageand a severity of a potential injury to the at least one occupant causedby the vehicle collision may be determined or estimated from processoranalysis performed by the one or more processors of (i) the occupantdata being generated or collected before, during, and/or after thevehicle collision; (ii) the external data being generated or collectedbefore, during, and/or after the vehicle collision; and/or (iii) thepositional information for at least one occupant of a vehicle before,during, and/or after the vehicle collision. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

Exemplary Extent of Injury Estimation

FIG. 11 illustrates a flow chart of an exemplary computer-implementedprocess of estimating an extent of injury to vehicle occupants resultingfrom a vehicle collision 1100. The method 1100 may include generatingand collecting sensor data regarding vehicle occupant positioning priorto, during, and/or after a vehicle collision from one or more occupantposition sensors 1102. The one or more occupant position sensors may bein-cabin vehicle-mounted sensors and/or mobile-device (e.g., wearables,smart phone, etc.) sensors.

The method 1100 may include determining a vehicle collision occurredfrom vehicle or mobile device sensor data, and estimating the impactforce and direction of impact force of the vehicle collision fromprocessor analysis of the vehicle and/or mobile device sensor data 1104.The method 1100 may include virtually reconstructing occupant bodypositioning with the vehicle prior to, during, and/or after the vehiclecollision based upon the occupant position sensor data 1106.

The method 1100 may include reconstructing a vehicle weight distributionprior to, during, and/or after the vehicle collision 1108. For instance,vehicle weight distribution calculated may include and/or account forseveral factors, such as occupant location and weight, seat occupied bythe occupant(s), location and weight of luggage or cargo, and vehiclesupport structure (e.g., seat position).

The method 1100 may include reconstructing occupant skeletal positioningprior to, during, and/or after the vehicle collision 1110. Occupantskeletal positioning may include and/or account for position of occupantjoins, spine, arms, legs, torso, neck, face, head, major bones, hands,feet, etc. Occupant skeletal positioning may also include, account for,and/or characterize occupant position as normal or face-forward sitting,reaching forward, reaching to the side or rear, torso or spine twisted,head or neck twisted or turned, size of occupant (adult, child, height,weight), etc.

The method 1100 may include estimating or calculating a likelihoodand/or type (or body location) of major injury to one or more occupantsbased upon (1) impact force and direction of force on the vehicle; (2)vehicle weight distribution; and/or (3) occupant skeletal positioningprior to, during, and/or after the vehicle collision 1112. For instance,the method 1000 may determine or estimate if there was an abnormalstress on joints or bones, such as determine if any broken bones likelyresulted from the vehicle collision.

The method 1100 may include if the likelihood of major injury to anyoccupant is greater than a pre-determined threshold (such as greaterthan 5, 10, or 20%). If so, the method 1100, may take corrective action1114. For instance, the method 1100 may request an ambulance and/ornotify a hospital via wireless communication or data transmission sentover one or more radio frequency links or communication channels.

Exemplary Autonomous Feature Selection & Engagement

In one aspect, a computer system for detecting a vehicular crash, and/orselecting an autonomous or semi-autonomous vehicle feature or system toengage to avoid or mitigate the vehicle collision may be provided. Thecomputer system may include one or more processors, sensors, and/ortransceivers in communication with at least one memory device. The oneor more processors, sensors, and/or transceivers may be programmed to:(1) receive occupant data from at least one internal sensor, theoccupant data being generated or collected prior to a vehicle collision;(2) receive external data from the at least one external sensor, theexternal data being generated or collected prior to the vehiclecollision; (3) determine that a potential vehicular crash is imminentbased upon the received external data; (4) determine positionalinformation for at least one occupant of a vehicle based upon theoccupant data; and/or (5) automatically engage an autonomous orsemi-autonomous vehicle feature or system to avoid the vehicle collisionor otherwise mitigate vehicle damage and/or occupant injury caused bythe vehicle collision.

The one or more processors, sensors, and/or transceivers may beconfigured or programmed to select an autonomous or semi-autonomousvehicle feature or system to engage based upon (i) the occupant data,(ii) the external data, and/or (iii) the positional information.Additionally or alternatively, the system may be configured to select anautonomous or semi-autonomous vehicle feature or system to engage basedupon (1) vehicle weight distribution; and/or (2) occupant skeletalpositioning prior to the vehicle collision (as determine from analysisof vehicle-mounted and/or mobile device sensor data, and discussed withrespect to FIG. 11). The system may include additional, less, oralternate functionality, including that discussed elsewhere herein.

In another aspect, a computer-based method for detecting a vehicularcrash and/or selecting an autonomous or semi-autonomous vehicle featureto engage may be provided. The method may be implemented on a vehiclecomputer device including one or more processors, sensors, and/ortransceivers in communication with at least one memory device. Themethod may include, via the one or more processors, sensors, and/ortransceivers: (1) receiving occupant data from at least one internalsensor; (2) receiving external data from the at least one externalsensor; (3) determining, by the vehicle computer device, that apotential vehicular crash is imminent based upon the received externaldata; and/or (4) automatically engaging an autonomous or semi-autonomousvehicle feature or system to avoid the vehicle collision or otherwisemitigate damage caused by the vehicle collision. The method may furtherinclude determining, via the one or more processors, sensors, and/ortransceivers, positional information for at least one occupant of avehicle based upon the occupant data.

The method may include selecting, via the one or more processors,sensors, and/or transceivers, an autonomous or semi-autonomous vehiclefeature or system to engage based upon (i) the occupant data, (ii) theexternal data, (iii) the positional information, and/or other sensordata. For instance, an amount of deceleration or force to apply to thebrakes may be determined based upon the (i) occupant data, (ii) externaldata, and/or (iii) positional information. Additionally oralternatively, the method may include selecting, via the one or moreprocessors, sensors, and/or transceivers, an autonomous orsemi-autonomous vehicle feature or system to engage based upon (1)vehicle weight distribution; and/or (2) occupant skeletal positioningprior to the vehicle collision (as determine from analysis ofvehicle-mounted and/or mobile device sensor data, and discussed withrespect to FIG. 11). The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

The types of autonomous or semi-autonomous vehicle-related functionalityor technology that may be used with the present embodiments to replacehuman driver actions may include and/or be related to the followingtypes of functionality: (a) fully autonomous (driverless); (b) limiteddriver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d)vehicle-to-infrastructure (and/or vice versa) wireless communication;(e) automatic or semi-automatic steering; (f) automatic orsemi-automatic acceleration; (g) automatic or semi-automatic braking;(h) automatic or semi-automatic blind spot monitoring; (i) automatic orsemi-automatic collision warning; (j) adaptive cruise control; (k)automatic or semi-automatic parking/parking assistance; (l) automatic orsemi-automatic collision preparation (windows roll up, seat adjustsupright, brakes pre-charge, etc.); (m) driver acuity/alertnessmonitoring; (n) pedestrian detection; (o) autonomous or semi-autonomousbackup systems; (p) road mapping systems; (q) software security andanti-hacking measures; (r) theft prevention/automatic return; (s)automatic or semi-automatic driving without occupants; and/or otherfunctionality.

For the method discussed directly above, the wirelesscommunication-based autonomous or semi-autonomous vehicle technology orfunctionality may include and/or be related to: automatic orsemi-automatic steering; automatic or semi-automatic acceleration and/orbraking; automatic or semi-automatic blind spot monitoring; automatic orsemi-automatic collision warning; adaptive cruise control; and/orautomatic or semi-automatic parking assistance. Additionally oralternatively, the autonomous or semi-autonomous technology orfunctionality may include and/or be related to: driver alertness orresponsive monitoring; pedestrian detection; artificial intelligenceand/or back-up systems; navigation or GPS-related systems; securityand/or anti-hacking measures; and/or theft prevention systems.

Exemplary Computer Device

FIG. 10 depicts a diagram 1000 of components of one or more exemplarycomputing devices 1010 that may be used in system 400 shown in FIG. 4.In some embodiments, computing device 1010 may be similar to AM server206 (shown in FIG. 2). Database 1020 may be coupled with severalseparate components within computing device 1010, which perform specifictasks. In this embodiment, database 1020 may include vehicular crashscenarios 1022, sensor data 1024, and/or insurance claim forms 1026. Insome embodiments, database 1020 is similar to database 420 (shown inFIG. 4).

Computing device 1010 may include the database 1020, as well as datastorage devices 1030. Computing device 1010 may also include acommunication component 1040 for receiving 305 sensor data, transmitting315 the scenario model, and receiving 320 a confirmation (all shown inFIG. 3). Computing device 1010 may further include a generatingcomponent 1050 for generating 310 a scenario model and generating 330 atleast one insurance claim form (both shown in FIG. 3). A processingcomponent 1060 may assist with execution of computer-executableinstructions associated with the system.

Exemplary Autonomous Systems

The types of autonomous or semi-autonomous vehicle-related functionalityor technology that may be used with the present embodiments to replacehuman driver actions may include and/or be related to the followingtypes of functionality: (a) fully autonomous (driverless); (b) limiteddriver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d)vehicle-to-infrastructure (and/or vice versa) wireless communication;(e) automatic or semi-automatic steering; (f) automatic orsemi-automatic acceleration; (g) automatic or semi-automatic braking;(h) automatic or semi-automatic blind spot monitoring; (i) automatic orsemi-automatic collision warning; (j) adaptive cruise control; (k)automatic or semi-automatic parking/parking assistance; (l) automatic orsemi-automatic collision preparation (windows roll up, seat adjustsupright, brakes pre-charge, etc.); (m) driver acuity/alertnessmonitoring; (n) pedestrian detection; (o) autonomous or semi-autonomousbackup systems; (p) road mapping systems; (q) software security andanti-hacking measures; (r) theft prevention/automatic return; (s)automatic or semi-automatic driving without occupants; and/or otherfunctionality.

For the method discussed directly above, the wirelesscommunication-based autonomous or semi-autonomous vehicle technology orfunctionality may include and/or be related to: automatic orsemi-automatic steering; automatic or semi-automatic acceleration and/orbraking; automatic or semi-automatic blind spot monitoring; automatic orsemi-automatic collision warning; adaptive cruise control; and/orautomatic or semi-automatic parking assistance. Additionally oralternatively, the autonomous or semi-autonomous technology orfunctionality may include and/or be related to: driver alertness orresponsive monitoring; pedestrian detection; artificial intelligenceand/or back-up systems; navigation or GPS-related systems; securityand/or anti-hacking measures; and/or theft prevention systems.

Below are some examples of autonomous or semi-autonomous vehicle-relatedfunctionality or technology replacing human driver action when AM server415 determines that a vehicular crash is imminent. AM server 415generates a scenario model of the potential vehicular crash anddetermines at least one potential injury to at least one occupant ofvehicle 100 based upon the scenario model. In a first example, AM server415 may determine that the occupant is facing the wrong direction andthat the angle of impact may cause serious injury to the occupant. AMserver 415 may sound a noise, such as a horn, to attract the attentionof the occupant and entice the occupant to change their direction offacing, such as towards the sounds.

In some examples, AM server 415 may initiate a change in the angle ofimpact of the vehicular impact to reduce potential injuries. Forexample, AM server 415 may cause change in the angle of impact to changethe forces that will affect the occupants of vehicle 100. AM server 415may also change where the impact will occur on vehicle 100. For example,AM server 415 may determine that it is better for the impact to occur atthe front of vehicle 100, where the engine is, instead of in the area ofthe passenger cabin. AM server 415 may also determine how to change theangle of impact to be a glancing blow instead of a direct collision.

To implement these changes, AM server 415 may instruct an autonomous orsemiautonomous system to activate to change the angle of impact. Forexample, AM server 415 may activate the steering to cause the wheels ofvehicle 100 to turn to cause the impact to be on a different part ofvehicle 100 or at a different angle. AM server 415 may also cause thebrakes to activate, or, counterintuitively, may cause the brakes to notactivate and instead accelerate vehicle 100 to change where the impactoccurs. In another example, AM server 415 may cause the airbags todeploy at different points in time during the vehicular impact basedupon the scenario model to reduce the severity of the potentialinjuries.

As described above, vehicle 100 may include a pedestrian detectionsystem that allows AM server 415 to determine the positions ofpedestrians around vehicle 100 and how the vehicular impact may affectthe pedestrians. AM server 415 may rotate or reposition vehicle toreduce the likelihood of injury to the pedestrians. In some examples, AMserver 415 may choose between impacting another vehicle and impacting astationary object. Based upon the scenario models, AM server 415 maydetermine that the potential injury to one or more occupants of vehiclemay be lessened by impacting the stationary objection. AM server 415 maycause vehicle 100 to steer into stationary object instead of a differentvehicle.

Exemplary Embodiments & Functionality

In one aspect, a computer system for reconstructing a vehicular crashmay be provided. The computer system may include at least one processorin communication with at least one memory device. The at least oneprocessor may be configured or programmed to: (1) receive sensor data ofa vehicular crash from at least one mobile device associated with auser; (2) generate a scenario model of the vehicular crash based uponthe received sensor data; (3) transmit the scenario model to a usercomputer device associated with the user; (4) receive a confirmation ofthe scenario model from the user computer device; (5) store the scenariomodel; and/or (6) generate at least one insurance claim form based uponthe scenario model to facilitate quickly and accurately processing aninsurance claim.

A further enhancement may be where the computer system may transmit amessage to one or more emergency services based upon the scenario model.The one or more emergency services may include, but are not limited to,a towing server, an emergency medical service provider, a firedepartment, a police department, and/or some other emergency responder.The computer system may select the one or more emergency services totransmit to based upon the scenario model and the location of thevehicular crash.

The computer system may achieve the above results by storing a databaseof vehicular crash scenarios based upon past vehicular crashes andsensor data associated with the vehicular crash scenarios. The computersystem may then compare the database of vehicular crash scenarios to thereceived sensor data and generate the scenario model of the vehicularcrash based upon the comparison. The computer system may also achievethe above results by generating a plurality of scenario models of thevehicular crash based upon the sensor data and the database of vehicularcrash scenarios. The computer system may determine a certainty of eachof the plurality of scenario models. The computer system may generatethe scenario model from the plurality of scenario models based upon thecertainty associated with the scenario model. A further enhancement maybe where the computer system may be configured to update the database ofvehicular crash scenarios based upon the stored scenario.

The mobile device described herein may be a one of the vehicle involvedin the vehicular crash, a cellular connected computer device, and anInternet connected computer device. The mobile device may include one ormore sensors.

The sensor data described herein may include, but is not limited to, ameasurement of at least one of speed, direction rate of acceleration,rate of deceleration, location, position, orientation, and rotation ofthe vehicle, and a measurement of one or more changes to at least one ofspeed, direction rate of acceleration, rate of deceleration, location,position, orientation, and rotation of the vehicle. The sensor data maybe based upon a period of time prior to the vehicular crash andcontinuing through to a period of time after the vehicular crash.

A further enhancement may be where the computer system may receivechanges to the scenario model from the user. The computer system mayupdate the scenario model based upon the user's changes.

Another enhancement may be where the computer system may determine aseverity for the vehicular accident based upon the scenario model. Thecomputer system may also be able to determine one or more potentialdamages to the vehicle based upon the scenario model.

A further enhancement may be where the computer system described hereinmay be configured to determine at least one potential injury of anoccupant of the vehicle based upon the scenario model. The computersystem may also transmit the at least one potential injury to the usercomputer device and receive confirmation of the at least one potentialinjury from the user computer device.

A further enhancement may be where the computer system may receivesensor data from a second vehicle involved in the vehicular crash. Thecomputer system may update the scenario model based upon the sensor datafrom the second vehicle.

Machine Learning & Other Matters

The computer-implemented methods discussed herein may includeadditional, less, or alternate actions, including those discussedelsewhere herein. The methods may be implemented via one or more localor remote processors, transceivers, and/or sensors (such as processors,transceivers, and/or sensors mounted on vehicles or mobile devices, orassociated with smart infrastructure or remote servers), and/or viacomputer-executable instructions stored on non-transitorycomputer-readable media or medium.

Additionally, the computer systems discussed herein may includeadditional, less, or alternate functionality, including that discussedelsewhere herein. The computer systems discussed herein may include orbe implemented via computer-executable instructions stored onnon-transitory computer-readable media or medium.

A processor or a processing element may be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, or a combined learning module or programthat learns in two or more fields or areas of interest. Machine learningmay involve identifying and recognizing patterns in existing data inorder to facilitate making predictions for subsequent data. Models maybe created based upon example inputs in order to make valid and reliablepredictions for novel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as image, mobile device, vehicle telematics, autonomous vehicle,and/or intelligent home telematics data. The machine learning programsmay utilize deep learning algorithms that may be primarily focused onpattern recognition, and may be trained after processing multipleexamples. The machine learning programs may include Bayesian programlearning (BPL), voice recognition and synthesis, image or objectrecognition, optical character recognition, and/or natural languageprocessing—either individually or in combination. The machine learningprograms may also include natural language processing, semanticanalysis, automatic reasoning, and/or machine learning.

In supervised machine learning, a processing element may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct output. Inunsupervised machine learning, the processing element may be required tofind its own structure in unlabeled example inputs. In one embodiment,machine learning techniques may be used to extract data about the mobiledevice or vehicle from device details, mobile device sensors,geolocation information, image data, and/or other data.

In one embodiment, a processing element may be trained by providing itwith a large sample of phone and/or online credentials with knowncharacteristics or features. Such information may include, for example,fingerprint, device print, verification codes, PBQA, and/or passivevoice analysis.

Based upon these analyses, the processing element may learn how toidentify characteristics and patterns that may then be applied toanalyzing sensor data, authentication data, image data, mobile devicedata, and/or other data. For example, the processing element may learn,with the user's permission or affirmative consent, to identify the userbased upon the user's device or login information. The processingelement may also learn how to identify different types of accidents andvehicular crashes based upon differences in the received sensor data.The processing element may further learn how to recreate a vehicularaccident based upon partial or incomplete information and determine alevel of certainty that the recreation is correct. As a result, at thetime of receiving accident data, providing automated reconstruction of avehicular accident, providing automated population of insurance claimforms, providing automated contact of emergency service personnel,providing information about the vehicular accident prior to the arrivalof the emergency service personnel on the scene, providing, and/orproviding automated detection of vehicular accidents as they areoccurring.

Additional Exemplary Embodiments

In still another aspect, a computer system for detecting a vehicularcrash may be provided. The computer system may include at least oneprocessor, sensor, and/or transceiver in communication with at least onememory device, the at least one processor, sensor, and/or transceiver.The at least one processor may be programmed to (1) receive data fromsaid at least one sensor; (2) determine that a potential vehicular crashis imminent based upon the received data; and/or (3) transmit one ormore high priority packets including a notification that the potentialvehicular crash is imminent. The computer system may include additional,less, or alternate functionality, including that discussed elsewhereherein.

For instance, the data from said at least one sensor may include speed,acceleration, braking, skidding, rate of deceleration, orientation, androtation information; and/or image data associated with an area forwardof a direction of travel of a vehicle that is acquired by a videorecorder or camera mounted on the vehicle. Determining that a potentialvehicular crash is imminent may be based upon applying objectrecognition techniques on the image data acquired by the video recorderor camera mounted on the vehicle. Determining that a potential vehicularcrash is imminent may further be based upon vehicle speed, acceleration,and braking data. Determining that a potential vehicular crash isimminent may be based upon processor analysis of vehicle speed andacceleration data, and the image data acquired by a vehicle mountedvideo recorder or camera.

Determining that a potential vehicular crash is imminent may be basedupon processor analysis of vehicle speed and acceleration data, andanalysis of the image data acquired by a vehicle mounted video recorderor camera that determines whether an object in a direction of travel ofthe vehicle is within a predetermined or threshold distance for thegiven vehicle speed and acceleration.

The sensor data may be analyzed to estimate a severity of the expectedvehicular crash, and the estimated severity of the expected vehicularcrash may be transmitted to a remote server via wireless communicationor data transmission over one or more radio links or wirelesscommunication channels.

The estimated severity of the expected vehicular crash may be determinedbased upon vehicle speed, acceleration, and braking data acquired frommobile device-mounted sensors and/or vehicle-mounted sensors, and a sizeand type of the object determined to be in the direction of travel ofthe vehicle from performing object recognition techniques on the imagedata captured by one or more vehicle-mounted cameras or video recorders.The type of the object determined to be in the direction of travel ofthe vehicle may be a compact vehicle, sport-utility vehicle, truck, orsemi-truck. The type of the object determined to be in the direction oftravel of the vehicle may be a concrete pillar or support, a streetsign, traffic light, or other road marking. The type of the objectdetermined to be in the direction of travel of the vehicle may be ananimal or a tree.

The estimated severity of the expected vehicular crash may be determinedto be an anticipated total loss, and a total loss insurance claimhandling process may be started by a remote server. The estimatedseverity of the expected vehicular crash may be used to start aninsurance claim handling process and/or used to pre-populate a virtualinsurance claim for insured review and/or approval.

In another aspect, a computer-implemented method of routing emergencyresponders to a vehicle collision, or otherwise provided notice of animminent vehicle collision, may be provided. The method may include (1)receiving, via one or more processors and/or transceivers that aremounted on a vehicle or mobile device traveling within the vehicle,sensor data from one or more sensors mounted on the vehicle or mobiledevice traveling within the vehicle; (2) determining, via the one ormore processors, that a vehicle collision is imminent (or likelyimminent) based upon analysis of the sensor data; (3) determining, viathe one or more processors, an estimated severity of the vehiclecollision based upon analysis of the sensor data; (4) determining, viathe one or more processors, whether the estimated severity is above apredetermined threshold; (5) if the estimated severity is above thepredetermined threshold, then, via the one or more processors,generating an electronic message detailing the imminent vehiclecollision; and (6) broadcasting, via the one or more processors and/ortransceivers, the electronic message to a remote server via wirelesscommunication or data transmission over one or more radio links orwireless communication channels to facilitate emergency respondersresponding to the vehicle collision expeditiously. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein, and may be implemented via one or more localor remote processors, servers, sensors, and/or transceivers, and/or viacomputer-executable instructions stored on non-transitorycomputer-readable medium or media.

For instance, the sensor data may include vehicle speed, acceleration,and braking information. The sensor data may further include image dataof area in a direction of vehicle travel or otherwise forward of themoving vehicle, the image data being acquired from one or more videorecorders or cameras mounted on the vehicle, a dashboard of the vehicle,or a mobile device traveling within the vehicle.

The method may include analyzing, via the one or more processors, theimage data using object recognition or pattern recognition techniques toidentify objects forward of the moving vehicle. The method may includeusing the results of the object recognition or pattern recognitiontechniques performed on the image data to identify type of objectsforward of the moving vehicle. The object forward of the moving vehicleidentified may be a compact vehicle, sport utility vehicle, or a truck.The object forward of the moving vehicle identified may be a concretepillar or support, a road sign, a traffic light, or mile marker. Theobject forward of the moving vehicle identified may be an animal or atree.

Determining, via the one or more processors, that a vehicle collision isimminent (or likely imminent) based upon analysis of the sensor data mayinclude processor analysis of vehicle speed and acceleration data, anddetermining whether or not an object shown in image data is within apredetermined distance of the vehicle. The one or more processors maydetermine that based upon the sensor data (such as vehicle speed,acceleration, and braking) and distance to an object shown in the imagedata that a collision will occur in 0.5 seconds, 1 second, 2 seconds, 3seconds, etc. For instance, a processor may determine that a vehiclecollision is imminent if it is likely to occur within 1-3 seconds.

Determining, via the one or more processors, an estimated severity ofthe vehicle collision based upon analysis of the sensor data may includeprocessor analysis of vehicle speed and acceleration data, anddetermining a size and type of an object shown in image data forward ofa direction of travel of the vehicle.

Determining, via the one or more processors, an estimated severity ofthe vehicle collision based upon analysis of the sensor data may includeprocessor analysis of vehicle speed and acceleration data, anddetermining a size and type of an object shown in image data forward ofa direction of travel of the vehicle, and a distance to the object.Determining, via the one or more processors, whether the estimatedseverity is above a predetermined threshold may include estimating anamount of vehicle damage from the vehicle collision and estimatingwhether or not the vehicle will be drivable or not.

Determining, via the one or more processors, whether the estimatedseverity is above a predetermined threshold may include determiningwhether or not the vehicle is expected to be a total loss or not.Generating an electronic message detailing the imminent vehiclecollision may indicate a GPS location of the vehicle, number ofpassengers, and type of object colliding with the vehicle. Generating anelectronic message detailing the imminent vehicle collision may indicatewhether the vehicle is expected or anticipated to be a total loss.Generating an electronic message detailing the imminent vehiclecollision may indicate whether the vehicle is expected or anticipated tobe a total loss, and the remote server begins a total loss handlingprocess. Additionally or alternatively, generating an electronic messagedetailing the imminent vehicle collision indicates an estimated amountof vehicle damage, and the remote server prepares a virtual insuranceclaim form for an insured's review and approval.

In another aspect, a computer system configured to provide notice toemergency responders, and/or route emergency responders to a vehiclecollision may be provided. The computer system may include one or moreprocessors, transceivers, and/or sensors mounted on a vehicle or amobile device traveling within the vehicle that are configured to: (1)receive or generate sensor data from one or more sensors mounted on thevehicle or mobile device traveling within the vehicle; (2) determinethat a vehicle collision is imminent (or likely imminent) based uponanalysis of the sensor data; (3) determine an estimated severity of thevehicle collision based upon analysis of the sensor data; (4) determinewhether the estimated severity is above a predetermined threshold; (5)if the estimated severity is above the predetermined threshold, thengenerate an electronic message detailing the imminent vehicle collision;and/or (6) broadcast the electronic message to a remote server viawireless communication or data transmission over one or more radio linksor wireless communication channels to facilitate emergency respondersresponding to the vehicle collision expeditiously. The computer systemmay include additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

In yet another aspect, a computer system for detecting a vehicular crashmay be provided. The computer system may include at least one processorin communication with at least one memory device, the at least oneprocessor. The at least one processor may be programmed to (1) (locallyor remotely) receive occupant data from at least one internal sensor(such as via wired or wireless communication); (2) (locally or remotely)receive external data from at least one external sensor (such as viawired or wireless communication); (3) determine that a potentialvehicular crash is imminent based upon the received external data; (4)determine positional information for at least one occupant of a vehicleand/or (5) perform at least one action to reduce a severity of apotential injury to the at least one occupant prior to impact. Themethod may include additional, less, or alternate actions, includingthose discussed elsewhere herein, and may be implemented via one or morelocal or remote processors, servers, sensors, and/or transceivers,and/or via computer-executable instructions stored on non-transitorycomputer-readable medium or media.

For instance, the data from the at least one external sensor may includespeed, acceleration, and braking information; and/or image dataassociated with an area forward of a direction of travel of a vehiclethat is acquired by a video recorder or camera mounted on the vehicle.Determining that a potential vehicular crash is imminent may be basedupon applying object recognition techniques on the image data acquiredby the video recorder or camera mounted on the vehicle. Determining thata potential vehicular crash is imminent may further be based uponvehicle speed, acceleration, deceleration, corning, and braking data.Determining that a potential vehicular crash is imminent may be basedupon processor analysis of vehicle speed and acceleration data, and theimage data acquired by a vehicle mounted video recorder or camera.

The processor may generate a model of the potential vehicular crashbased upon the received data to further analyze. The processor may alsodetermine a position and a direction of facing of at least one occupantof the vehicle and use the model to determine an advantageous directionof facing for the at least one occupant. If one of the occupants is notfacing in an advantageous way, the processor may generate a soundthrough the audio system to cause the at least one occupant to change tothe advantageous direction of facing. The processor may also cause aseat or a portion of a seat of an occupant to move, shift, and/or rotateto change the position and/or direction of facing of the occupant.

Other corrective actions may be taken by the vehicle or a vehiclecontroller after a vehicle collision is determined to be imminent orpotentially imminent. In one embodiment, the processor may be furtherprogrammed to automatically engage an autonomous or semi-autonomousvehicle feature or system to mitigate damage and/or injury caused by thevehicle collision.

For instance, if the vehicle is an autonomous vehicle, the vehiclecontroller may take control of the vehicle (such as from a human driverdriving the vehicle) and maneuver the vehicle to avoid the collision,such as by braking, accelerating, or swerving to avoid another vehicle.Other vehicle maneuvers automatically directed by the autonomous vehicleafter a vehicle collision is determined to be likely may mitigate thedamage to the vehicle and/or injuries to vehicle occupant. For instance,the vehicle may automatically brake and/or deploy air bags.

The types of autonomous or semi-autonomous vehicle-related functionalityor technology that may be used with the present embodiments to replacehuman driver actions may include and/or be related to the followingtypes of functionality: (a) fully autonomous (driverless); (b) limiteddriver control; (c) vehicle-to-vehicle (V2V) wireless communication; (d)vehicle-to-infrastructure (and/or vice versa) wireless communication;(e) automatic or semi-automatic steering; (f) automatic orsemi-automatic acceleration; (g) automatic or semi-automatic braking;(h) automatic or semi-automatic blind spot monitoring; (i) automatic orsemi-automatic collision warning; (j) adaptive cruise control; (k)automatic or semi-automatic parking/parking assistance; (l) automatic orsemi-automatic collision preparation (windows roll up, seat adjustsupright, brakes pre-charge, etc.); (m) driver acuity/alertnessmonitoring; (n) pedestrian detection; (o) autonomous or semi-autonomousbackup systems; (p) road mapping systems; (q) software security andanti-hacking measures; (r) theft prevention/automatic return; (s)automatic or semi-automatic driving without occupants; and/or otherfunctionality.

For instance, the wireless communication-based autonomous orsemi-autonomous vehicle technology or functionality may include and/orbe related to: automatic or semi-automatic steering; automatic orsemi-automatic acceleration and/or braking; automatic or semi-automaticblind spot monitoring; automatic or semi-automatic collision warning;adaptive cruise control; and/or automatic or semi-automatic parkingassistance. Additionally or alternatively, the autonomous orsemi-autonomous technology or functionality may include and/or berelated to: driver alertness or responsive monitoring; pedestriandetection; artificial intelligence and/or back-up systems; navigation orGPS-related systems; security and/or anti-hacking measures; and/or theftprevention systems.

ADDITIONAL CONSIDERATIONS

The present embodiments may facilitate avoiding vehicle collisions, orotherwise mitigating damage and injuries caused by vehicle collisions.Thus, vehicles configured with the functionality and computer systemsmay have a lower level of risk than conventional vehicles. Therefore,lower insurance premiums and/or insurance discounts may be generated andprovided to insured's owning vehicles configured with the functionalityand/or computer systems discussed herein.

As will be appreciated based upon the foregoing specification, theabove-described embodiments of the disclosure may be implemented usingcomputer programming or engineering techniques including computersoftware, firmware, hardware or any combination or subset thereof. Anysuch resulting program, having computer-readable code means, may beembodied or provided within one or more computer-readable media, therebymaking a computer program product, i.e., an article of manufacture,according to the discussed embodiments of the disclosure. Thecomputer-readable media may be, for example, but is not limited to, afixed (hard) drive, diskette, optical disk, magnetic tape, semiconductormemory such as read-only memory (ROM), and/or any transmitting/receivingmedium, such as the Internet or other communication network or link. Thearticle of manufacture containing the computer code may be made and/orused by executing the code directly from one medium, by copying the codefrom one medium to another medium, or by transmitting the code over anetwork.

These computer programs (also known as programs, software, softwareapplications, “apps”, or code) include machine instructions for aprogrammable processor, and can be implemented in a high-levelprocedural and/or object-oriented programming language, and/or inassembly/machine language. As used herein, the terms “machine-readablemedium” “computer-readable medium” refers to any computer programproduct, apparatus and/or device (e.g., magnetic discs, optical disks,memory, Programmable Logic Devices (PLDs)) used to provide machineinstructions and/or data to a programmable processor, including amachine-readable medium that receives machine instructions as amachine-readable signal. The “machine-readable medium” and“computer-readable medium,” however, do not include transitory signals.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

As used herein, a processor may include any programmable systemincluding systems using micro-controllers, reduced instruction setcircuits (RISC), application specific integrated circuits (ASICs), logiccircuits, and any other circuit or processor capable of executing thefunctions described herein. The above examples are example only, and arethus not intended to limit in any way the definition and/or meaning ofthe term “processor.”

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution by aprocessor, including RAM memory, ROM memory, EPROM memory, EEPROMmemory, and non-volatile RAM (NVRAM) memory. The above memory types areexample only, and are thus not limiting as to the types of memory usablefor storage of a computer program.

In one embodiment, a computer program is provided, and the program isembodied on a computer readable medium. In an exemplary embodiment, thesystem is executed on a single computer system, without requiring aconnection to a sever computer. In a further embodiment, the system isbeing run in a Windows® environment (Windows is a registered trademarkof Microsoft Corporation, Redmond, Wash.). In yet another embodiment,the system is run on a mainframe environment and a UNIX® serverenvironment (UNIX is a registered trademark of X/Open Company Limitedlocated in Reading, Berkshire, United Kingdom). The application isflexible and designed to run in various different environments withoutcompromising any major functionality.

In some embodiments, the system includes multiple components distributedamong a plurality of computing devices. One or more components may be inthe form of computer-executable instructions embodied in acomputer-readable medium. The systems and processes are not limited tothe specific embodiments described herein. In addition, components ofeach system and each process can be practiced independent and separatefrom other components and processes described herein. Each component andprocess can also be used in combination with other assembly packages andprocesses.

As used herein, an element or step recited in the singular and precededby the word “a” or “an” should be understood as not excluding pluralelements or steps, unless such exclusion is explicitly recited.Furthermore, references to “example embodiment” or “one embodiment” ofthe present disclosure are not intended to be interpreted as excludingthe existence of additional embodiments that also incorporate therecited features.

The patent claims at the end of this document are not intended to beconstrued under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being expressly recited in the claim(s).

This written description uses examples to disclose the disclosure,including the best mode, and also to enable any person skilled in theart to practice the disclosure, including making and using any devicesor systems and performing any incorporated methods. The patentable scopeof the disclosure is defined by the claims, and may include otherexamples that occur to those skilled in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal language of the claims.

We claim:
 1. A computer system for reconstructing a vehicular crash, thecomputer system including at least one processor in communication withat least one memory device, the at least one processor is configured to:monitor, by a plurality of sensors embedded both within a vehicle and atleast one mobile device associated with a user within the vehicle,sensor data of a vehicular crash including current surroundings andoperating conditions of the vehicle, wherein the vehicular crashinvolves the vehicle that includes the user and the at least one mobiledevice, wherein the sensor data is based upon a period of time prior toa vehicular crash involving the vehicle and continuing through to aperiod of time after the vehicular crash, wherein the sensor dataincludes a current state of connectivity of the plurality of sensors,and wherein receiving a data stream of the sensor data validates thecurrent state of connectivity; collect, from a vehicle controller andthe at least one mobile device via a wireless communication channel, thesensor data of the vehicular crash; generate a scenario model of thevehicular crash based upon the collected sensor data, wherein thescenario model recreates an accident scene of the vehicular crashincluding at least damage to the vehicle, wherein the scenario modelincludes a damage amount to the vehicle calculated based on a) a loss ofconnectivity of at least one sensor of the plurality of sensors includedin the current state of connectivity, and b) a required amount of traumacorresponding to the loss of connectivity, wherein the scenario model isfurther based upon information from at least vehicle crash testingfacilities and past analyzed crashes; determine at least one potentialinjury to an occupant of the vehicle based upon the scenario model,wherein the at least one potential injury is associated with therequired amount of trauma corresponding to the loss of connectivity;retrieve, from the at least one memory device, the damage amount to thevehicle from the scenario model to populate data fields included withinat least one virtual insurance claim form; and populate data fields ofat least one insurance claim form based upon the at least one virtualinsurance claim form.
 2. The computer system of claim 1, wherein theprocessor is further configured to: transmit a message to one or moreemergency services based upon the scenario model, wherein the one ormore emergency services include at least one of a towing service, anemergency medical service provider, a police department, a firedepartment, and an emergency responder.
 3. The computer system of claim2, wherein the processor is further configured to: select the one ormore emergency services based upon the scenario model and a location ofthe vehicular crash.
 4. The computer system of claim 1, wherein the atleast one mobile device includes a cellular Internet connected computerdevice.
 5. The computer system of claim 1, wherein the sensor dataincludes a measurement of at least one of speed, direction rate ofacceleration, rate of deceleration, location, position, orientation, androtation of the vehicle, and wherein the sensor data further includes anupdated measurement of one or more changes to at least one of speed,direction rate of acceleration, rate of deceleration, location,position, orientation, and rotation of the vehicle.
 6. The computersystem of claim 1, wherein the sensor data includes a number ofoccupants in the vehicle, seatbelt sensor data, and seat occupant weightsensor data.
 7. The computer system of claim 1, wherein the processor isfurther configured to: store a database of vehicular crash scenariosbased upon past vehicular crashes and past sensor data associated withthe vehicular crash scenarios; compare the database of vehicular crashscenarios to the collected sensor data; and generate the scenario modelof the vehicular crash based upon the comparison.
 8. The computer systemof claim 7, wherein the processor is further configured to: generate aplurality of scenario models of the vehicular crash based upon thecollected sensor data and the database of vehicular crash scenarios;determine a degree of certainty of each of the plurality of scenariomodels; and generate the scenario model from the plurality of scenariomodels based upon the degree of certainty associated with the scenariomodel.
 9. The computer system of claim 7, wherein the processor isfurther configured to: update the database of vehicular crash scenariosbased upon the scenario model.
 10. The computer system of claim 1,wherein the processor is further configured to: transmit the at leastone potential injury to a user computer device; and receive confirmationof the at least one potential injury from the user computer device. 11.The computer system of claim 1, wherein the processor is furtherconfigured to: receive a second set of sensor data from a differentvehicle involved in the vehicular crash; and update the scenario modelbased upon the second set of sensor data from the different vehicle. 12.The computer system of claim 1, wherein the processor is furtherconfigured to: transmit a message to one or more emergency services,wherein the message includes the at least one potential injury to theoccupant of the vehicle, and wherein the message requests immediatedeployment of the one or more emergency services for treatment of the atleast one potential injury.
 13. A computer-based method forreconstructing a vehicular crash, the computer-based method implementedon an accident monitoring (“AM”) server including at least one processorin communication with at least one memory device, the computer-basedmethod comprising: monitoring, by a plurality of sensors embedded bothwithin a vehicle and at least one mobile device associated with a userwithin the vehicle, sensor data of a vehicular crash including currentsurroundings and operating conditions of the vehicle, wherein thevehicular crash involves the vehicle that includes the user and the atleast one mobile device, wherein the sensor data is based upon a periodof time prior to a vehicular crash involving the vehicle and continuingthrough to a period of time after the vehicular crash, wherein thesensor data includes a current state of connectivity of the plurality ofsensors, and wherein receiving a data stream of the sensor datavalidates the current state of connectivity; collecting, at the AMserver from a vehicle controller and the at least one mobile device viaa wireless communication channel, the sensor data of the vehicularcrash; generating, by the AM server, a scenario model of the vehicularcrash based upon the collected sensor data, wherein the scenario modelrecreates an accident scene of the vehicular crash including at leastdamage to the vehicle, wherein the scenario model includes a damageamount to the vehicle calculated based on a) a loss of connectivity ofat least one sensor of the plurality of sensors included in the currentstate of connectivity, and b) a required amount of trauma correspondingto the loss of connectivity, wherein the scenario model is further basedupon information from at least vehicle crash testing facilities and pastanalyzed crashes; determining, by the AM server, at least one potentialinjury to an occupant of the vehicle based upon the scenario model,wherein the at least one potential injury is associated with therequired amount of trauma corresponding to the loss of connectivity;retrieving, by the AM server from the at least one memory device, thedamage amount to the vehicle from the scenario model to populate datafields included within at least one virtual insurance claim form; andpopulating, by the AM server, data field of at least one insurance claimform based upon the at least one virtual insurance claim form.
 14. Thecomputer-based method of claim 13 further comprising: transmitting, bythe AM server, a message to one or more emergency services based uponthe scenario model, wherein the one or more emergency services includeat least one of a towing service, an emergency medical service provider,a police department, a fire department, and an emergency responder. 15.The computer-based method of claim 14 further comprising: selecting, bythe AM server, the one or more emergency services based upon thescenario model and a location of the vehicular crash.
 16. Thecomputer-based method of claim 13, wherein the sensor data includes anumber of occupants in the vehicle, seatbelt sensor data, and seatoccupant weight sensor data, and wherein the sensor data also includes ameasurement of at least one of speed, direction rate of acceleration,rate of deceleration, location, position, orientation, and rotation ofthe vehicle, and wherein the sensor data further includes an updatedmeasurement of one or more changes to at least one of speed, directionrate of acceleration, rate of deceleration, location, position,orientation, and rotation of the vehicle.
 17. The computer-based methodof claim 13 further comprising: storing, by the AM server, a database ofvehicular crash scenarios based upon past vehicular crashes and pastsensor data associated with the vehicular crash scenarios; comparing, bythe AM server, the database of vehicular crash scenarios to thecollected sensor data; and generating, by the AM server, the scenariomodel of the vehicular crash based upon the comparison.
 18. Thecomputer-based method of claim 17 further comprising: generating, by theAM server, a plurality of scenario models of the vehicular crash basedupon the collected sensor data and the database of vehicular crashscenarios; determining, by the AM server, a degree of certainty of eachof the plurality of scenario models; and generating, by the AM server,the scenario model from the plurality of scenario models based upon thedegree of certainty associated with the scenario model.
 19. Thecomputer-based method of claim 17 further comprising: updating, by theAM server, the database of vehicular crash scenarios based upon thescenario model.